deploying docs (apache/tvm@c0abab769ff152d87f84963f18a98d2f7c9bdf31)
diff --git a/docs/_sources/how_to/compile_models/from_darknet.rst.txt b/docs/_sources/how_to/compile_models/from_darknet.rst.txt
index f158fbc..93d63ce 100644
--- a/docs/_sources/how_to/compile_models/from_darknet.rst.txt
+++ b/docs/_sources/how_to/compile_models/from_darknet.rst.txt
@@ -318,7 +318,7 @@
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 1 minutes  33.619 seconds)
+   **Total running time of the script:** ( 1 minutes  35.148 seconds)
 
 
 .. _sphx_glr_download_how_to_compile_models_from_darknet.py:
diff --git a/docs/_sources/how_to/compile_models/from_keras.rst.txt b/docs/_sources/how_to/compile_models/from_keras.rst.txt
index 5dd8b12..771435d 100644
--- a/docs/_sources/how_to/compile_models/from_keras.rst.txt
+++ b/docs/_sources/how_to/compile_models/from_keras.rst.txt
Binary files differ
diff --git a/docs/_sources/how_to/compile_models/from_oneflow.rst.txt b/docs/_sources/how_to/compile_models/from_oneflow.rst.txt
index 0e5a8bc..035fad9 100644
--- a/docs/_sources/how_to/compile_models/from_oneflow.rst.txt
+++ b/docs/_sources/how_to/compile_models/from_oneflow.rst.txt
Binary files differ
diff --git a/docs/_sources/how_to/compile_models/from_paddle.rst.txt b/docs/_sources/how_to/compile_models/from_paddle.rst.txt
index 2cbcdcc..e56aa01 100644
--- a/docs/_sources/how_to/compile_models/from_paddle.rst.txt
+++ b/docs/_sources/how_to/compile_models/from_paddle.rst.txt
@@ -209,7 +209,7 @@
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 1 minutes  3.080 seconds)
+   **Total running time of the script:** ( 1 minutes  4.067 seconds)
 
 
 .. _sphx_glr_download_how_to_compile_models_from_paddle.py:
diff --git a/docs/_sources/how_to/compile_models/from_pytorch.rst.txt b/docs/_sources/how_to/compile_models/from_pytorch.rst.txt
index 074274e..51e9d76 100644
--- a/docs/_sources/how_to/compile_models/from_pytorch.rst.txt
+++ b/docs/_sources/how_to/compile_models/from_pytorch.rst.txt
Binary files differ
diff --git a/docs/_sources/how_to/compile_models/from_tensorflow.rst.txt b/docs/_sources/how_to/compile_models/from_tensorflow.rst.txt
index a1064cf..ef60ccf 100644
--- a/docs/_sources/how_to/compile_models/from_tensorflow.rst.txt
+++ b/docs/_sources/how_to/compile_models/from_tensorflow.rst.txt
@@ -424,7 +424,7 @@
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 1 minutes  28.778 seconds)
+   **Total running time of the script:** ( 1 minutes  34.391 seconds)
 
 
 .. _sphx_glr_download_how_to_compile_models_from_tensorflow.py:
diff --git a/docs/_sources/how_to/compile_models/sg_execution_times.rst.txt b/docs/_sources/how_to/compile_models/sg_execution_times.rst.txt
index 2b21eac..410433e 100644
--- a/docs/_sources/how_to/compile_models/sg_execution_times.rst.txt
+++ b/docs/_sources/how_to/compile_models/sg_execution_times.rst.txt
@@ -5,24 +5,24 @@
 
 Computation times
 =================
-**06:37.173** total execution time for **how_to_compile_models** files:
+**06:53.245** total execution time for **how_to_compile_models** files:
 
 +-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_darknet.py` (``from_darknet.py``)       | 01:33.619 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_darknet.py` (``from_darknet.py``)       | 01:35.148 | 0.0 MB |
 +-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_tensorflow.py` (``from_tensorflow.py``) | 01:28.778 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_tensorflow.py` (``from_tensorflow.py``) | 01:34.391 | 0.0 MB |
 +-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_paddle.py` (``from_paddle.py``)         | 01:03.080 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_paddle.py` (``from_paddle.py``)         | 01:04.067 | 0.0 MB |
 +-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_oneflow.py` (``from_oneflow.py``)       | 00:43.948 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_oneflow.py` (``from_oneflow.py``)       | 00:45.119 | 0.0 MB |
 +-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_coreml.py` (``from_coreml.py``)         | 00:38.565 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_coreml.py` (``from_coreml.py``)         | 00:40.743 | 0.0 MB |
 +-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_pytorch.py` (``from_pytorch.py``)       | 00:28.671 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_pytorch.py` (``from_pytorch.py``)       | 00:31.295 | 0.0 MB |
 +-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_keras.py` (``from_keras.py``)           | 00:25.980 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_keras.py` (``from_keras.py``)           | 00:27.264 | 0.0 MB |
 +-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_tflite.py` (``from_tflite.py``)         | 00:11.726 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_tflite.py` (``from_tflite.py``)         | 00:12.142 | 0.0 MB |
 +-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_onnx.py` (``from_onnx.py``)             | 00:02.806 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_onnx.py` (``from_onnx.py``)             | 00:03.077 | 0.0 MB |
 +-----------------------------------------------------------------------------------+-----------+--------+
diff --git a/docs/_sources/how_to/deploy_models/deploy_model_on_adreno.rst.txt b/docs/_sources/how_to/deploy_models/deploy_model_on_adreno.rst.txt
index b443c69..7613f19 100644
--- a/docs/_sources/how_to/deploy_models/deploy_model_on_adreno.rst.txt
+++ b/docs/_sources/how_to/deploy_models/deploy_model_on_adreno.rst.txt
@@ -673,7 +673,7 @@
     Evaluate inference time cost...
     Execution time summary:
      mean (ms)   median (ms)    max (ms)     min (ms)     std (ms)  
-     3992.3590    3991.7039    3996.1809    3990.2026      1.9953                  
+     3996.9880    3996.4937    4002.4632    3994.9470      2.0188                  
 
 
 
@@ -681,7 +681,7 @@
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 1 minutes  17.902 seconds)
+   **Total running time of the script:** ( 1 minutes  18.592 seconds)
 
 
 .. _sphx_glr_download_how_to_deploy_models_deploy_model_on_adreno.py:
diff --git a/docs/_sources/how_to/deploy_models/deploy_model_on_adreno_tvmc.rst.txt b/docs/_sources/how_to/deploy_models/deploy_model_on_adreno_tvmc.rst.txt
index 2b6daab..294cd8d 100644
--- a/docs/_sources/how_to/deploy_models/deploy_model_on_adreno_tvmc.rst.txt
+++ b/docs/_sources/how_to/deploy_models/deploy_model_on_adreno_tvmc.rst.txt
Binary files differ
diff --git a/docs/_sources/how_to/deploy_models/deploy_model_on_android.rst.txt b/docs/_sources/how_to/deploy_models/deploy_model_on_android.rst.txt
index 33e3760..3e56324 100644
--- a/docs/_sources/how_to/deploy_models/deploy_model_on_android.rst.txt
+++ b/docs/_sources/how_to/deploy_models/deploy_model_on_android.rst.txt
@@ -437,7 +437,7 @@
     Evaluate inference time cost...
     Execution time summary:
      mean (ms)   median (ms)    max (ms)     min (ms)     std (ms)  
-      13.8279      13.7426      14.3332      13.6757       0.2071                  
+      14.1003      13.9759      14.6276      13.7279       0.3297                  
 
 
 
diff --git a/docs/_sources/how_to/deploy_models/deploy_object_detection_pytorch.rst.txt b/docs/_sources/how_to/deploy_models/deploy_object_detection_pytorch.rst.txt
index 446193f..520324a 100644
--- a/docs/_sources/how_to/deploy_models/deploy_object_detection_pytorch.rst.txt
+++ b/docs/_sources/how_to/deploy_models/deploy_object_detection_pytorch.rst.txt
Binary files differ
diff --git a/docs/_sources/how_to/deploy_models/deploy_prequantized.rst.txt b/docs/_sources/how_to/deploy_models/deploy_prequantized.rst.txt
index 31c9b06..0354ebc 100644
--- a/docs/_sources/how_to/deploy_models/deploy_prequantized.rst.txt
+++ b/docs/_sources/how_to/deploy_models/deploy_prequantized.rst.txt
Binary files differ
diff --git a/docs/_sources/how_to/deploy_models/deploy_prequantized_tflite.rst.txt b/docs/_sources/how_to/deploy_models/deploy_prequantized_tflite.rst.txt
index 5cfb451..c287c5c 100644
--- a/docs/_sources/how_to/deploy_models/deploy_prequantized_tflite.rst.txt
+++ b/docs/_sources/how_to/deploy_models/deploy_prequantized_tflite.rst.txt
@@ -423,7 +423,7 @@
 
     Execution time summary:
      mean (ms)   median (ms)    max (ms)     min (ms)     std (ms)  
-      102.5848     102.4513     108.2271     102.0635      0.6542                  
+      106.9010     106.9923     107.4698     105.7611      0.3254                  
 
 
 
diff --git a/docs/_sources/how_to/deploy_models/sg_execution_times.rst.txt b/docs/_sources/how_to/deploy_models/sg_execution_times.rst.txt
index 0d58af5..e849d13 100644
--- a/docs/_sources/how_to/deploy_models/sg_execution_times.rst.txt
+++ b/docs/_sources/how_to/deploy_models/sg_execution_times.rst.txt
@@ -5,24 +5,24 @@
 
 Computation times
 =================
-**09:34.1000** total execution time for **how_to_deploy_models** files:
+**09:49.401** total execution time for **how_to_deploy_models** files:
 
 +------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_deploy_models_deploy_object_detection_pytorch.py` (``deploy_object_detection_pytorch.py``) | 03:21.329 | 0.0 MB |
+| :ref:`sphx_glr_how_to_deploy_models_deploy_object_detection_pytorch.py` (``deploy_object_detection_pytorch.py``) | 03:27.717 | 0.0 MB |
 +------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_deploy_models_deploy_prequantized.py` (``deploy_prequantized.py``)                         | 01:28.654 | 0.0 MB |
+| :ref:`sphx_glr_how_to_deploy_models_deploy_prequantized.py` (``deploy_prequantized.py``)                         | 01:30.670 | 0.0 MB |
 +------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_adreno.py` (``deploy_model_on_adreno.py``)                   | 01:17.902 | 0.0 MB |
+| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_adreno.py` (``deploy_model_on_adreno.py``)                   | 01:18.592 | 0.0 MB |
 +------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_android.py` (``deploy_model_on_android.py``)                 | 00:51.056 | 0.0 MB |
+| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_android.py` (``deploy_model_on_android.py``)                 | 00:52.756 | 0.0 MB |
 +------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_deploy_models_deploy_prequantized_tflite.py` (``deploy_prequantized_tflite.py``)           | 00:49.836 | 0.0 MB |
+| :ref:`sphx_glr_how_to_deploy_models_deploy_prequantized_tflite.py` (``deploy_prequantized_tflite.py``)           | 00:51.596 | 0.0 MB |
 +------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_adreno_tvmc.py` (``deploy_model_on_adreno_tvmc.py``)         | 00:46.982 | 0.0 MB |
+| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_adreno_tvmc.py` (``deploy_model_on_adreno_tvmc.py``)         | 00:48.012 | 0.0 MB |
 +------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_nano.py` (``deploy_model_on_nano.py``)                       | 00:29.708 | 0.0 MB |
+| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_nano.py` (``deploy_model_on_nano.py``)                       | 00:30.123 | 0.0 MB |
 +------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_rasp.py` (``deploy_model_on_rasp.py``)                       | 00:29.526 | 0.0 MB |
+| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_rasp.py` (``deploy_model_on_rasp.py``)                       | 00:29.927 | 0.0 MB |
 +------------------------------------------------------------------------------------------------------------------+-----------+--------+
 | :ref:`sphx_glr_how_to_deploy_models_deploy_sparse.py` (``deploy_sparse.py``)                                     | 00:00.007 | 0.0 MB |
 +------------------------------------------------------------------------------------------------------------------+-----------+--------+
diff --git a/docs/_sources/how_to/extend_tvm/sg_execution_times.rst.txt b/docs/_sources/how_to/extend_tvm/sg_execution_times.rst.txt
index d17b91f..a8f6009 100644
--- a/docs/_sources/how_to/extend_tvm/sg_execution_times.rst.txt
+++ b/docs/_sources/how_to/extend_tvm/sg_execution_times.rst.txt
@@ -5,12 +5,12 @@
 
 Computation times
 =================
-**00:03.888** total execution time for **how_to_extend_tvm** files:
+**00:04.110** total execution time for **how_to_extend_tvm** files:
 
 +-------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_extend_tvm_use_pass_instrument.py` (``use_pass_instrument.py``)     | 00:02.697 | 0.0 MB |
+| :ref:`sphx_glr_how_to_extend_tvm_use_pass_instrument.py` (``use_pass_instrument.py``)     | 00:02.858 | 0.0 MB |
 +-------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_extend_tvm_use_pass_infra.py` (``use_pass_infra.py``)               | 00:01.185 | 0.0 MB |
+| :ref:`sphx_glr_how_to_extend_tvm_use_pass_infra.py` (``use_pass_infra.py``)               | 00:01.244 | 0.0 MB |
 +-------------------------------------------------------------------------------------------+-----------+--------+
 | :ref:`sphx_glr_how_to_extend_tvm_low_level_custom_pass.py` (``low_level_custom_pass.py``) | 00:00.007 | 0.0 MB |
 +-------------------------------------------------------------------------------------------+-----------+--------+
diff --git a/docs/_sources/how_to/extend_tvm/use_pass_instrument.rst.txt b/docs/_sources/how_to/extend_tvm/use_pass_instrument.rst.txt
index 3374811..e85cfa5 100644
--- a/docs/_sources/how_to/extend_tvm/use_pass_instrument.rst.txt
+++ b/docs/_sources/how_to/extend_tvm/use_pass_instrument.rst.txt
@@ -220,10 +220,10 @@
  .. code-block:: none
 
     Printing results of timing profile...
-    InferType: 24488us [24488us] (48.67%; 48.67%)
-    FoldScaleAxis: 25824us [7us] (51.33%; 51.33%)
-            FoldConstant: 25818us [1751us] (51.32%; 99.97%)
-                    InferType: 24067us [24067us] (47.83%; 93.22%)
+    InferType: 24692us [24692us] (48.49%; 48.49%)
+    FoldScaleAxis: 26231us [9us] (51.51%; 51.51%)
+            FoldConstant: 26222us [1763us] (51.49%; 99.97%)
+                    InferType: 24458us [24458us] (48.03%; 93.28%)
 
 
 
@@ -262,10 +262,10 @@
  .. code-block:: none
 
     Printing results of timing profile...
-    InferType: 24538us [24538us] (49.36%; 49.36%)
-    FoldScaleAxis: 25177us [6us] (50.64%; 50.64%)
-            FoldConstant: 25171us [1657us] (50.63%; 99.98%)
-                    InferType: 23514us [23514us] (47.30%; 93.42%)
+    InferType: 24394us [24394us] (48.35%; 48.35%)
+    FoldScaleAxis: 26058us [8us] (51.65%; 51.65%)
+            FoldConstant: 26050us [1803us] (51.63%; 99.97%)
+                    InferType: 24247us [24247us] (48.06%; 93.08%)
 
 
 
diff --git a/docs/_sources/how_to/optimize_operators/opt_conv_cuda.rst.txt b/docs/_sources/how_to/optimize_operators/opt_conv_cuda.rst.txt
index 30cc20f..4db5931 100644
--- a/docs/_sources/how_to/optimize_operators/opt_conv_cuda.rst.txt
+++ b/docs/_sources/how_to/optimize_operators/opt_conv_cuda.rst.txt
@@ -331,7 +331,7 @@
 
  .. code-block:: none
 
-    Convolution: 53.506336 ms
+    Convolution: 35.825408 ms
 
 
 
diff --git a/docs/_sources/how_to/optimize_operators/opt_conv_tensorcore.rst.txt b/docs/_sources/how_to/optimize_operators/opt_conv_tensorcore.rst.txt
index 41c29b4..36ab2fe 100644
--- a/docs/_sources/how_to/optimize_operators/opt_conv_tensorcore.rst.txt
+++ b/docs/_sources/how_to/optimize_operators/opt_conv_tensorcore.rst.txt
@@ -598,7 +598,7 @@
 
  .. code-block:: none
 
-    conv2d with tensor core: 11.243111 ms
+    conv2d with tensor core: 12.201779 ms
 
 
 
diff --git a/docs/_sources/how_to/optimize_operators/opt_gemm.rst.txt b/docs/_sources/how_to/optimize_operators/opt_gemm.rst.txt
index eb54f6c..f3c5bc9 100644
--- a/docs/_sources/how_to/optimize_operators/opt_gemm.rst.txt
+++ b/docs/_sources/how_to/optimize_operators/opt_gemm.rst.txt
@@ -134,8 +134,8 @@
 
  .. code-block:: none
 
-    Numpy running time: 0.013599
-    Baseline: 3.114235
+    Numpy running time: 0.013816
+    Baseline: 3.272670
 
 
 
@@ -227,7 +227,7 @@
 
  .. code-block:: none
 
-    Opt1: 0.291226
+    Opt1: 0.290892
 
 
 
@@ -318,7 +318,7 @@
 
  .. code-block:: none
 
-    Opt2: 0.269819
+    Opt2: 0.268008
 
 
 
@@ -406,7 +406,7 @@
 
  .. code-block:: none
 
-    Opt3: 0.115345
+    Opt3: 0.108291
 
 
 
@@ -523,7 +523,7 @@
 
  .. code-block:: none
 
-    Opt4: 0.105055
+    Opt4: 0.102623
 
 
 
@@ -635,7 +635,7 @@
 
  .. code-block:: none
 
-    Opt5: 0.095636
+    Opt5: 0.093889
 
 
 
@@ -748,7 +748,7 @@
 
  .. code-block:: none
 
-    Opt6: 0.114014
+    Opt6: 0.112948
 
 
 
diff --git a/docs/_sources/how_to/optimize_operators/sg_execution_times.rst.txt b/docs/_sources/how_to/optimize_operators/sg_execution_times.rst.txt
index eed693b..888e075 100644
--- a/docs/_sources/how_to/optimize_operators/sg_execution_times.rst.txt
+++ b/docs/_sources/how_to/optimize_operators/sg_execution_times.rst.txt
@@ -5,12 +5,12 @@
 
 Computation times
 =================
-**00:31.420** total execution time for **how_to_optimize_operators** files:
+**00:31.445** total execution time for **how_to_optimize_operators** files:
 
 +-----------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_optimize_operators_opt_gemm.py` (``opt_gemm.py``)                       | 00:28.299 | 0.0 MB |
+| :ref:`sphx_glr_how_to_optimize_operators_opt_gemm.py` (``opt_gemm.py``)                       | 00:28.398 | 0.0 MB |
 +-----------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_optimize_operators_opt_conv_tensorcore.py` (``opt_conv_tensorcore.py``) | 00:01.947 | 0.0 MB |
+| :ref:`sphx_glr_how_to_optimize_operators_opt_conv_tensorcore.py` (``opt_conv_tensorcore.py``) | 00:01.939 | 0.0 MB |
 +-----------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_optimize_operators_opt_conv_cuda.py` (``opt_conv_cuda.py``)             | 00:01.175 | 0.0 MB |
+| :ref:`sphx_glr_how_to_optimize_operators_opt_conv_cuda.py` (``opt_conv_cuda.py``)             | 00:01.108 | 0.0 MB |
 +-----------------------------------------------------------------------------------------------+-----------+--------+
diff --git a/docs/_sources/how_to/tune_with_autoscheduler/sg_execution_times.rst.txt b/docs/_sources/how_to/tune_with_autoscheduler/sg_execution_times.rst.txt
index 05af8f9..14e2814 100644
--- a/docs/_sources/how_to/tune_with_autoscheduler/sg_execution_times.rst.txt
+++ b/docs/_sources/how_to/tune_with_autoscheduler/sg_execution_times.rst.txt
@@ -5,18 +5,18 @@
 
 Computation times
 =================
-**03:35.339** total execution time for **how_to_tune_with_autoscheduler** files:
+**03:39.725** total execution time for **how_to_tune_with_autoscheduler** files:
 
 +----------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_x86.py` (``tune_network_x86.py``)             | 01:33.597 | 0.0 MB |
+| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_x86.py` (``tune_network_x86.py``)             | 01:34.792 | 0.0 MB |
 +----------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_cuda.py` (``tune_network_cuda.py``)           | 01:10.747 | 0.0 MB |
+| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_cuda.py` (``tune_network_cuda.py``)           | 01:11.824 | 0.0 MB |
 +----------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_arm.py` (``tune_network_arm.py``)             | 00:19.548 | 0.0 MB |
+| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_arm.py` (``tune_network_arm.py``)             | 00:20.072 | 0.0 MB |
 +----------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_mali.py` (``tune_network_mali.py``)           | 00:16.541 | 0.0 MB |
+| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_mali.py` (``tune_network_mali.py``)           | 00:17.432 | 0.0 MB |
 +----------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_conv2d_layer_cuda.py` (``tune_conv2d_layer_cuda.py``) | 00:14.807 | 0.0 MB |
+| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_conv2d_layer_cuda.py` (``tune_conv2d_layer_cuda.py``) | 00:15.506 | 0.0 MB |
 +----------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_sparse_x86.py` (``tune_sparse_x86.py``)               | 00:00.098 | 0.0 MB |
+| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_sparse_x86.py` (``tune_sparse_x86.py``)               | 00:00.100 | 0.0 MB |
 +----------------------------------------------------------------------------------------------------------+-----------+--------+
diff --git a/docs/_sources/how_to/tune_with_autoscheduler/tune_conv2d_layer_cuda.rst.txt b/docs/_sources/how_to/tune_with_autoscheduler/tune_conv2d_layer_cuda.rst.txt
index b7609a7..5359a48 100644
--- a/docs/_sources/how_to/tune_with_autoscheduler/tune_conv2d_layer_cuda.rst.txt
+++ b/docs/_sources/how_to/tune_with_autoscheduler/tune_conv2d_layer_cuda.rst.txt
@@ -766,7 +766,7 @@
 
  .. code-block:: none
 
-    Execution time of this operator: 0.350 ms
+    Execution time of this operator: 0.352 ms
 
 
 
diff --git a/docs/_sources/how_to/tune_with_autoscheduler/tune_network_cuda.rst.txt b/docs/_sources/how_to/tune_with_autoscheduler/tune_network_cuda.rst.txt
index 2fb2dad..e3ca060 100644
--- a/docs/_sources/how_to/tune_with_autoscheduler/tune_network_cuda.rst.txt
+++ b/docs/_sources/how_to/tune_with_autoscheduler/tune_network_cuda.rst.txt
@@ -633,7 +633,7 @@
     Evaluate inference time cost...
     Execution time summary:
      mean (ms)   median (ms)    max (ms)     min (ms)     std (ms)  
-       3.2481       3.2516       3.2531       3.2395       0.0061                  
+       3.2637       3.2651       3.2659       3.2601       0.0025                  
 
 
 
@@ -660,7 +660,7 @@
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 1 minutes  10.747 seconds)
+   **Total running time of the script:** ( 1 minutes  11.824 seconds)
 
 
 .. _sphx_glr_download_how_to_tune_with_autoscheduler_tune_network_cuda.py:
diff --git a/docs/_sources/how_to/tune_with_autoscheduler/tune_network_x86.rst.txt b/docs/_sources/how_to/tune_with_autoscheduler/tune_network_x86.rst.txt
index 86e1f93..8924541 100644
--- a/docs/_sources/how_to/tune_with_autoscheduler/tune_network_x86.rst.txt
+++ b/docs/_sources/how_to/tune_with_autoscheduler/tune_network_x86.rst.txt
@@ -655,7 +655,7 @@
     Evaluate inference time cost...
     Execution time summary:
      mean (ms)   median (ms)    max (ms)     min (ms)     std (ms)  
-      715.0204     714.7141     716.8798     713.4673      1.4099                  
+      705.0103     703.5363     708.5367     702.9581      2.5046                  
 
 
 
@@ -682,7 +682,7 @@
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 1 minutes  33.597 seconds)
+   **Total running time of the script:** ( 1 minutes  34.792 seconds)
 
 
 .. _sphx_glr_download_how_to_tune_with_autoscheduler_tune_network_x86.py:
diff --git a/docs/_sources/how_to/tune_with_autotvm/sg_execution_times.rst.txt b/docs/_sources/how_to/tune_with_autotvm/sg_execution_times.rst.txt
index 8b5cdf3..b907c43 100644
--- a/docs/_sources/how_to/tune_with_autotvm/sg_execution_times.rst.txt
+++ b/docs/_sources/how_to/tune_with_autotvm/sg_execution_times.rst.txt
@@ -5,16 +5,16 @@
 
 Computation times
 =================
-**00:23.554** total execution time for **how_to_tune_with_autotvm** files:
+**00:23.036** total execution time for **how_to_tune_with_autotvm** files:
 
 +--------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_tune_with_autotvm_tune_conv2d_cuda.py` (``tune_conv2d_cuda.py``)           | 00:23.517 | 0.0 MB |
+| :ref:`sphx_glr_how_to_tune_with_autotvm_tune_conv2d_cuda.py` (``tune_conv2d_cuda.py``)           | 00:22.987 | 0.0 MB |
 +--------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_tune_with_autotvm_tune_relay_x86.py` (``tune_relay_x86.py``)               | 00:00.020 | 0.0 MB |
+| :ref:`sphx_glr_how_to_tune_with_autotvm_tune_relay_x86.py` (``tune_relay_x86.py``)               | 00:00.033 | 0.0 MB |
 +--------------------------------------------------------------------------------------------------+-----------+--------+
 | :ref:`sphx_glr_how_to_tune_with_autotvm_tune_relay_mobile_gpu.py` (``tune_relay_mobile_gpu.py``) | 00:00.006 | 0.0 MB |
 +--------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_tune_with_autotvm_tune_relay_arm.py` (``tune_relay_arm.py``)               | 00:00.005 | 0.0 MB |
+| :ref:`sphx_glr_how_to_tune_with_autotvm_tune_relay_arm.py` (``tune_relay_arm.py``)               | 00:00.006 | 0.0 MB |
 +--------------------------------------------------------------------------------------------------+-----------+--------+
 | :ref:`sphx_glr_how_to_tune_with_autotvm_tune_relay_cuda.py` (``tune_relay_cuda.py``)             | 00:00.005 | 0.0 MB |
 +--------------------------------------------------------------------------------------------------+-----------+--------+
diff --git a/docs/_sources/how_to/tune_with_autotvm/tune_conv2d_cuda.rst.txt b/docs/_sources/how_to/tune_with_autotvm/tune_conv2d_cuda.rst.txt
index 4226383..8b7f2ef 100644
--- a/docs/_sources/how_to/tune_with_autotvm/tune_conv2d_cuda.rst.txt
+++ b/docs/_sources/how_to/tune_with_autotvm/tune_conv2d_cuda.rst.txt
@@ -326,7 +326,7 @@
 
     Best config:
     ,None
-    Time cost of this operator: 0.037074
+    Time cost of this operator: 0.037033
 
 
 
diff --git a/docs/_sources/how_to/work_with_microtvm/micro_autotune.rst.txt b/docs/_sources/how_to/work_with_microtvm/micro_autotune.rst.txt
index 8f3b6f4..e72eef9 100644
--- a/docs/_sources/how_to/work_with_microtvm/micro_autotune.rst.txt
+++ b/docs/_sources/how_to/work_with_microtvm/micro_autotune.rst.txt
@@ -360,10 +360,10 @@
     ########## Build without Autotuning ##########
     Node Name                                     Ops                                           Time(us)  Time(%)  Shape              Inputs  Outputs  Measurements(us)  
     ---------                                     ---                                           --------  -------  -----              ------  -------  ----------------  
-    tvmgen_default_fused_nn_contrib_conv2d_NCHWc  tvmgen_default_fused_nn_contrib_conv2d_NCHWc  296.6     98.753   (1, 2, 10, 10, 3)  2       1        [296.6]           
-    tvmgen_default_fused_layout_transform_1       tvmgen_default_fused_layout_transform_1       2.799     0.932    (1, 6, 10, 10)     1       1        [2.799]           
-    tvmgen_default_fused_layout_transform         tvmgen_default_fused_layout_transform         0.947     0.315    (1, 1, 10, 10, 3)  1       1        [0.947]           
-    Total_time                                    -                                             300.346   -        -                  -       -        -                 
+    tvmgen_default_fused_nn_contrib_conv2d_NCHWc  tvmgen_default_fused_nn_contrib_conv2d_NCHWc  296.4     98.759   (1, 2, 10, 10, 3)  2       1        [296.4]           
+    tvmgen_default_fused_layout_transform_1       tvmgen_default_fused_layout_transform_1       2.786     0.928    (1, 6, 10, 10)     1       1        [2.786]           
+    tvmgen_default_fused_layout_transform         tvmgen_default_fused_layout_transform         0.938     0.313    (1, 1, 10, 10, 3)  1       1        [0.938]           
+    Total_time                                    -                                             300.124   -        -                  -       -        -                 
 
 
 
@@ -428,10 +428,10 @@
     ########## Build with Autotuning ##########
     Node Name                                     Ops                                           Time(us)  Time(%)  Shape              Inputs  Outputs  Measurements(us)  
     ---------                                     ---                                           --------  -------  -----              ------  -------  ----------------  
-    tvmgen_default_fused_nn_contrib_conv2d_NCHWc  tvmgen_default_fused_nn_contrib_conv2d_NCHWc  100.5     97.396   (1, 6, 10, 10, 1)  2       1        [100.5]           
-    tvmgen_default_fused_layout_transform_1       tvmgen_default_fused_layout_transform_1       1.752     1.698    (1, 6, 10, 10)     1       1        [1.752]           
-    tvmgen_default_fused_layout_transform         tvmgen_default_fused_layout_transform         0.935     0.906    (1, 3, 10, 10, 1)  1       1        [0.935]           
-    Total_time                                    -                                             103.187   -        -                  -       -        -                 
+    tvmgen_default_fused_nn_contrib_conv2d_NCHWc  tvmgen_default_fused_nn_contrib_conv2d_NCHWc  100.5     97.541   (1, 6, 10, 10, 1)  2       1        [100.5]           
+    tvmgen_default_fused_layout_transform_1       tvmgen_default_fused_layout_transform_1       1.707     1.657    (1, 6, 10, 10)     1       1        [1.707]           
+    tvmgen_default_fused_layout_transform         tvmgen_default_fused_layout_transform         0.827     0.802    (1, 3, 10, 10, 1)  1       1        [0.827]           
+    Total_time                                    -                                             103.033   -        -                  -       -        -                 
 
 
 
@@ -439,7 +439,7 @@
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 1 minutes  21.930 seconds)
+   **Total running time of the script:** ( 1 minutes  24.297 seconds)
 
 
 .. _sphx_glr_download_how_to_work_with_microtvm_micro_autotune.py:
diff --git a/docs/_sources/how_to/work_with_microtvm/micro_pytorch.rst.txt b/docs/_sources/how_to/work_with_microtvm/micro_pytorch.rst.txt
index d259101..405a752 100644
--- a/docs/_sources/how_to/work_with_microtvm/micro_pytorch.rst.txt
+++ b/docs/_sources/how_to/work_with_microtvm/micro_pytorch.rst.txt
Binary files differ
diff --git a/docs/_sources/how_to/work_with_microtvm/micro_train.rst.txt b/docs/_sources/how_to/work_with_microtvm/micro_train.rst.txt
index 86925c3..87909ec 100644
--- a/docs/_sources/how_to/work_with_microtvm/micro_train.rst.txt
+++ b/docs/_sources/how_to/work_with_microtvm/micro_train.rst.txt
@@ -218,7 +218,7 @@
  .. code-block:: none
 
 
-    '/tmp/tmpdr97tapa/images/random'
+    '/tmp/tmpwre49770/images/random'
 
 
 
@@ -318,8 +318,8 @@
 
  .. code-block:: none
 
-    /tmp/tmpdr97tapa/images/target contains 8144 images
-    /tmp/tmpdr97tapa/images/random contains 5000 images
+    /tmp/tmpwre49770/images/target contains 8144 images
+    /tmp/tmpwre49770/images/random contains 5000 images
 
 
 
@@ -494,13 +494,13 @@
  .. code-block:: none
 
     Epoch 1/3
-    328/328 - 42s - loss: 0.2088 - accuracy: 0.9248 - val_loss: 0.1196 - val_accuracy: 0.9615 - 42s/epoch - 128ms/step
+    328/328 - 43s - loss: 0.2252 - accuracy: 0.9240 - val_loss: 0.1183 - val_accuracy: 0.9596 - 43s/epoch - 130ms/step
     Epoch 2/3
-    328/328 - 40s - loss: 0.1049 - accuracy: 0.9618 - val_loss: 0.0922 - val_accuracy: 0.9705 - 40s/epoch - 121ms/step
+    328/328 - 40s - loss: 0.0966 - accuracy: 0.9637 - val_loss: 0.1127 - val_accuracy: 0.9524 - 40s/epoch - 121ms/step
     Epoch 3/3
-    328/328 - 40s - loss: 0.0690 - accuracy: 0.9734 - val_loss: 0.0994 - val_accuracy: 0.9668 - 40s/epoch - 121ms/step
+    328/328 - 40s - loss: 0.0657 - accuracy: 0.9741 - val_loss: 0.1188 - val_accuracy: 0.9619 - 40s/epoch - 121ms/step
 
-    <keras.callbacks.History object at 0x7f7af33be850>
+    <keras.callbacks.History object at 0x7f61e04e99a0>
 
 
 
@@ -861,7 +861,7 @@
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 4 minutes  31.311 seconds)
+   **Total running time of the script:** ( 4 minutes  28.413 seconds)
 
 
 .. _sphx_glr_download_how_to_work_with_microtvm_micro_train.py:
diff --git a/docs/_sources/how_to/work_with_microtvm/sg_execution_times.rst.txt b/docs/_sources/how_to/work_with_microtvm/sg_execution_times.rst.txt
index 0d9af5a..37fc5ea 100644
--- a/docs/_sources/how_to/work_with_microtvm/sg_execution_times.rst.txt
+++ b/docs/_sources/how_to/work_with_microtvm/sg_execution_times.rst.txt
@@ -5,20 +5,20 @@
 
 Computation times
 =================
-**07:50.096** total execution time for **how_to_work_with_microtvm** files:
+**07:53.347** total execution time for **how_to_work_with_microtvm** files:
 
 +-----------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_microtvm_micro_train.py` (``micro_train.py``)           | 04:31.311 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_microtvm_micro_train.py` (``micro_train.py``)           | 04:28.413 | 0.0 MB |
 +-----------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_microtvm_micro_pytorch.py` (``micro_pytorch.py``)       | 01:27.097 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_microtvm_micro_pytorch.py` (``micro_pytorch.py``)       | 01:30.219 | 0.0 MB |
 +-----------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_microtvm_micro_autotune.py` (``micro_autotune.py``)     | 01:21.930 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_microtvm_micro_autotune.py` (``micro_autotune.py``)     | 01:24.297 | 0.0 MB |
 +-----------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_microtvm_micro_aot.py` (``micro_aot.py``)               | 00:11.563 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_microtvm_micro_aot.py` (``micro_aot.py``)               | 00:11.736 | 0.0 MB |
 +-----------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_microtvm_micro_custom_ide.py` (``micro_custom_ide.py``) | 00:10.053 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_microtvm_micro_custom_ide.py` (``micro_custom_ide.py``) | 00:10.498 | 0.0 MB |
 +-----------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_microtvm_micro_tflite.py` (``micro_tflite.py``)         | 00:08.142 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_microtvm_micro_tflite.py` (``micro_tflite.py``)         | 00:08.185 | 0.0 MB |
 +-----------------------------------------------------------------------------------------+-----------+--------+
 | :ref:`sphx_glr_how_to_work_with_microtvm_micro_ethosu.py` (``micro_ethosu.py``)         | 00:00.000 | 0.0 MB |
 +-----------------------------------------------------------------------------------------+-----------+--------+
diff --git a/docs/_sources/how_to/work_with_relay/sg_execution_times.rst.txt b/docs/_sources/how_to/work_with_relay/sg_execution_times.rst.txt
index 3de56fa..8cad9aa 100644
--- a/docs/_sources/how_to/work_with_relay/sg_execution_times.rst.txt
+++ b/docs/_sources/how_to/work_with_relay/sg_execution_times.rst.txt
@@ -5,14 +5,14 @@
 
 Computation times
 =================
-**00:38.653** total execution time for **how_to_work_with_relay** files:
+**00:39.698** total execution time for **how_to_work_with_relay** files:
 
 +----------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_relay_using_pipeline_executor.py` (``using_pipeline_executor.py``) | 00:32.874 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_relay_using_pipeline_executor.py` (``using_pipeline_executor.py``) | 00:33.794 | 0.0 MB |
 +----------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_relay_using_external_lib.py` (``using_external_lib.py``)           | 00:03.690 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_relay_using_external_lib.py` (``using_external_lib.py``)           | 00:03.867 | 0.0 MB |
 +----------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_relay_build_gcn.py` (``build_gcn.py``)                             | 00:02.082 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_relay_build_gcn.py` (``build_gcn.py``)                             | 00:02.031 | 0.0 MB |
 +----------------------------------------------------------------------------------------------------+-----------+--------+
 | :ref:`sphx_glr_how_to_work_with_relay_using_relay_viz.py` (``using_relay_viz.py``)                 | 00:00.006 | 0.0 MB |
 +----------------------------------------------------------------------------------------------------+-----------+--------+
diff --git a/docs/_sources/how_to/work_with_schedules/intrin_math.rst.txt b/docs/_sources/how_to/work_with_schedules/intrin_math.rst.txt
index c95d945..032fd34 100644
--- a/docs/_sources/how_to/work_with_schedules/intrin_math.rst.txt
+++ b/docs/_sources/how_to/work_with_schedules/intrin_math.rst.txt
@@ -281,7 +281,7 @@
  .. code-block:: none
 
 
-    <function my_cuda_math_rule at 0x7f7b1a646ee0>
+    <function my_cuda_math_rule at 0x7f6225012790>
 
 
 
diff --git a/docs/_sources/how_to/work_with_schedules/sg_execution_times.rst.txt b/docs/_sources/how_to/work_with_schedules/sg_execution_times.rst.txt
index 280a22a..b1ac338 100644
--- a/docs/_sources/how_to/work_with_schedules/sg_execution_times.rst.txt
+++ b/docs/_sources/how_to/work_with_schedules/sg_execution_times.rst.txt
@@ -5,22 +5,22 @@
 
 Computation times
 =================
-**00:05.307** total execution time for **how_to_work_with_schedules** files:
+**00:05.497** total execution time for **how_to_work_with_schedules** files:
 
 +------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_schedules_intrin_math.py` (``intrin_math.py``)                 | 00:02.321 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_schedules_intrin_math.py` (``intrin_math.py``)                 | 00:02.389 | 0.0 MB |
 +------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_schedules_tensorize.py` (``tensorize.py``)                     | 00:01.384 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_schedules_tensorize.py` (``tensorize.py``)                     | 00:01.468 | 0.0 MB |
 +------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_schedules_reduction.py` (``reduction.py``)                     | 00:00.671 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_schedules_reduction.py` (``reduction.py``)                     | 00:00.690 | 0.0 MB |
 +------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_schedules_scan.py` (``scan.py``)                               | 00:00.656 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_schedules_scan.py` (``scan.py``)                               | 00:00.672 | 0.0 MB |
 +------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_schedules_extern_op.py` (``extern_op.py``)                     | 00:00.115 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_schedules_extern_op.py` (``extern_op.py``)                     | 00:00.116 | 0.0 MB |
 +------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_schedules_schedule_primitives.py` (``schedule_primitives.py``) | 00:00.069 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_schedules_schedule_primitives.py` (``schedule_primitives.py``) | 00:00.068 | 0.0 MB |
 +------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_work_with_schedules_tedd.py` (``tedd.py``)                               | 00:00.061 | 0.0 MB |
+| :ref:`sphx_glr_how_to_work_with_schedules_tedd.py` (``tedd.py``)                               | 00:00.063 | 0.0 MB |
 +------------------------------------------------------------------------------------------------+-----------+--------+
 | :ref:`sphx_glr_how_to_work_with_schedules_tuple_inputs.py` (``tuple_inputs.py``)               | 00:00.031 | 0.0 MB |
 +------------------------------------------------------------------------------------------------+-----------+--------+
diff --git a/docs/_sources/topic/vta/tutorials/frontend/deploy_detection.rst.txt b/docs/_sources/topic/vta/tutorials/frontend/deploy_detection.rst.txt
index d500d6b..ace3501 100644
--- a/docs/_sources/topic/vta/tutorials/frontend/deploy_detection.rst.txt
+++ b/docs/_sources/topic/vta/tutorials/frontend/deploy_detection.rst.txt
@@ -337,7 +337,7 @@
 
     /workspace/python/tvm/relay/build_module.py:345: DeprecationWarning: Please use input parameter mod (tvm.IRModule) instead of deprecated parameter mod (tvm.relay.function.Function)
       warnings.warn(
-    yolov3-tiny inference graph built in 26.78s!
+    yolov3-tiny inference graph built in 28.32s!
 
 
 
@@ -445,6 +445,11 @@
 
 
 
+.. rst-class:: sphx-glr-timing
+
+   **Total running time of the script:** ( 1 minutes  0.981 seconds)
+
+
 .. _sphx_glr_download_topic_vta_tutorials_frontend_deploy_detection.py:
 
 .. only:: html
diff --git a/docs/_sources/topic/vta/tutorials/frontend/sg_execution_times.rst.txt b/docs/_sources/topic/vta/tutorials/frontend/sg_execution_times.rst.txt
index 4f3a3db..c6b6668 100644
--- a/docs/_sources/topic/vta/tutorials/frontend/sg_execution_times.rst.txt
+++ b/docs/_sources/topic/vta/tutorials/frontend/sg_execution_times.rst.txt
@@ -5,8 +5,8 @@
 
 Computation times
 =================
-**00:58.758** total execution time for **topic_vta_tutorials_frontend** files:
+**01:00.981** total execution time for **topic_vta_tutorials_frontend** files:
 
 +--------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_topic_vta_tutorials_frontend_deploy_detection.py` (``deploy_detection.py``) | 00:58.758 | 0.0 MB |
+| :ref:`sphx_glr_topic_vta_tutorials_frontend_deploy_detection.py` (``deploy_detection.py``) | 01:00.981 | 0.0 MB |
 +--------------------------------------------------------------------------------------------+-----------+--------+
diff --git a/docs/_sources/topic/vta/tutorials/optimize/sg_execution_times.rst.txt b/docs/_sources/topic/vta/tutorials/optimize/sg_execution_times.rst.txt
index cb8989c..8fa9ae8 100644
--- a/docs/_sources/topic/vta/tutorials/optimize/sg_execution_times.rst.txt
+++ b/docs/_sources/topic/vta/tutorials/optimize/sg_execution_times.rst.txt
@@ -5,10 +5,10 @@
 
 Computation times
 =================
-**00:03.203** total execution time for **topic_vta_tutorials_optimize** files:
+**00:03.253** total execution time for **topic_vta_tutorials_optimize** files:
 
 +--------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_topic_vta_tutorials_optimize_convolution_opt.py` (``convolution_opt.py``)         | 00:02.750 | 0.0 MB |
+| :ref:`sphx_glr_topic_vta_tutorials_optimize_convolution_opt.py` (``convolution_opt.py``)         | 00:02.776 | 0.0 MB |
 +--------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_topic_vta_tutorials_optimize_matrix_multiply_opt.py` (``matrix_multiply_opt.py``) | 00:00.454 | 0.0 MB |
+| :ref:`sphx_glr_topic_vta_tutorials_optimize_matrix_multiply_opt.py` (``matrix_multiply_opt.py``) | 00:00.477 | 0.0 MB |
 +--------------------------------------------------------------------------------------------------+-----------+--------+
diff --git a/docs/_sources/topic/vta/tutorials/sg_execution_times.rst.txt b/docs/_sources/topic/vta/tutorials/sg_execution_times.rst.txt
index 392c871..7c851d7 100644
--- a/docs/_sources/topic/vta/tutorials/sg_execution_times.rst.txt
+++ b/docs/_sources/topic/vta/tutorials/sg_execution_times.rst.txt
@@ -5,10 +5,10 @@
 
 Computation times
 =================
-**00:00.732** total execution time for **topic_vta_tutorials** files:
+**00:00.770** total execution time for **topic_vta_tutorials** files:
 
 +---------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_topic_vta_tutorials_matrix_multiply.py` (``matrix_multiply.py``) | 00:00.380 | 0.0 MB |
+| :ref:`sphx_glr_topic_vta_tutorials_matrix_multiply.py` (``matrix_multiply.py``) | 00:00.403 | 0.0 MB |
 +---------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_topic_vta_tutorials_vta_get_started.py` (``vta_get_started.py``) | 00:00.352 | 0.0 MB |
+| :ref:`sphx_glr_topic_vta_tutorials_vta_get_started.py` (``vta_get_started.py``) | 00:00.366 | 0.0 MB |
 +---------------------------------------------------------------------------------+-----------+--------+
diff --git a/docs/_sources/tutorial/auto_scheduler_matmul_x86.rst.txt b/docs/_sources/tutorial/auto_scheduler_matmul_x86.rst.txt
index 0e2b941..7172064 100644
--- a/docs/_sources/tutorial/auto_scheduler_matmul_x86.rst.txt
+++ b/docs/_sources/tutorial/auto_scheduler_matmul_x86.rst.txt
@@ -318,7 +318,7 @@
 
  .. code-block:: none
 
-    Execution time of this operator: 90.821 ms
+    Execution time of this operator: 91.787 ms
 
 
 
@@ -434,7 +434,7 @@
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 1 minutes  20.519 seconds)
+   **Total running time of the script:** ( 1 minutes  21.545 seconds)
 
 
 .. _sphx_glr_download_tutorial_auto_scheduler_matmul_x86.py:
diff --git a/docs/_sources/tutorial/autotvm_matmul_x86.rst.txt b/docs/_sources/tutorial/autotvm_matmul_x86.rst.txt
index 3658b55..efa522c 100644
--- a/docs/_sources/tutorial/autotvm_matmul_x86.rst.txt
+++ b/docs/_sources/tutorial/autotvm_matmul_x86.rst.txt
@@ -454,16 +454,16 @@
     waiting for device...
     device available
     Get devices for measurement successfully!
-    No: 1   GFLOPS: 12.88/12.88     result: MeasureResult(costs=(0.0208434224,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.6094293594360352, timestamp=1719180823.0064042)       [('tile_y', [-1, 2]), ('tile_x', [-1, 256])],None,81
-    No: 2   GFLOPS: 2.03/12.88      result: MeasureResult(costs=(0.13226104600000002,), error_no=MeasureErrorNo.NO_ERROR, all_cost=2.376148223876953, timestamp=1719180825.3805306) [('tile_y', [-1, 512]), ('tile_x', [-1, 4])],None,29
-    No: 3   GFLOPS: 8.74/12.88      result: MeasureResult(costs=(0.030701681400000003,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.7432756423950195, timestamp=1719180826.1278791)       [('tile_y', [-1, 1]), ('tile_x', [-1, 32])],None,50
-    No: 4   GFLOPS: 9.95/12.88      result: MeasureResult(costs=(0.026989392199999997,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.686959981918335, timestamp=1719180826.821211) [('tile_y', [-1, 512]), ('tile_x', [-1, 256])],None,89
-    No: 5   GFLOPS: 1.78/12.88      result: MeasureResult(costs=(0.1512033644,), error_no=MeasureErrorNo.NO_ERROR, all_cost=2.6725265979766846, timestamp=1719180829.6383104)       [('tile_y', [-1, 4]), ('tile_x', [-1, 1])],None,2
-    No: 6   GFLOPS: 11.62/12.88     result: MeasureResult(costs=(0.0231033506,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.647376298904419, timestamp=1719180830.268541) [('tile_y', [-1, 256]), ('tile_x', [-1, 256])],None,88
-    No: 7   GFLOPS: 13.72/13.72     result: MeasureResult(costs=(0.0195589724,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.58542799949646, timestamp=1719180830.83535)   [('tile_y', [-1, 256]), ('tile_x', [-1, 64])],None,68
-    No: 8   GFLOPS: 12.48/13.72     result: MeasureResult(costs=(0.021502260000000002,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.6178991794586182, timestamp=1719180831.4312394)       [('tile_y', [-1, 16]), ('tile_x', [-1, 16])],None,44
-    No: 9   GFLOPS: 5.53/13.72      result: MeasureResult(costs=(0.0485592874,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.9927237033843994, timestamp=1719180832.5525236)       [('tile_y', [-1, 1]), ('tile_x', [-1, 4])],None,20
-    No: 10  GFLOPS: 14.92/14.92     result: MeasureResult(costs=(0.0179899578,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.5298264026641846, timestamp=1719180833.103099)        [('tile_y', [-1, 32]), ('tile_x', [-1, 128])],None,75
+    No: 1   GFLOPS: 2.10/2.10       result: MeasureResult(costs=(0.12757850639999999,), error_no=MeasureErrorNo.NO_ERROR, all_cost=2.3357033729553223, timestamp=1719239482.3309703)        [('tile_y', [-1, 128]), ('tile_x', [-1, 4])],None,27
+    No: 2   GFLOPS: 9.23/9.23       result: MeasureResult(costs=(0.0290866322,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.7264056205749512, timestamp=1719239483.056599)        [('tile_y', [-1, 1]), ('tile_x', [-1, 32])],None,50
+    No: 3   GFLOPS: 10.63/10.63     result: MeasureResult(costs=(0.0252520154,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.7592840194702148, timestamp=1719239483.72897) [('tile_y', [-1, 8]), ('tile_x', [-1, 128])],None,73
+    No: 4   GFLOPS: 14.14/14.14     result: MeasureResult(costs=(0.0189836802,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.5697653293609619, timestamp=1719239484.2919142)       [('tile_y', [-1, 256]), ('tile_x', [-1, 64])],None,68
+    No: 5   GFLOPS: 12.81/14.14     result: MeasureResult(costs=(0.020957730799999998,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.6032514572143555, timestamp=1719239485.0401525)       [('tile_y', [-1, 2]), ('tile_x', [-1, 256])],None,81
+    No: 6   GFLOPS: 0.96/14.14      result: MeasureResult(costs=(0.2808536046,), error_no=MeasureErrorNo.NO_ERROR, all_cost=4.739181280136108, timestamp=1719239489.7784312)        [('tile_y', [-1, 512]), ('tile_x', [-1, 2])],None,19
+    No: 7   GFLOPS: 12.16/14.14     result: MeasureResult(costs=(0.022070074000000002,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.6491849422454834, timestamp=1719239490.3936822)       [('tile_y', [-1, 4]), ('tile_x', [-1, 128])],None,72
+    No: 8   GFLOPS: 2.47/14.14      result: MeasureResult(costs=(0.10888049340000001,), error_no=MeasureErrorNo.NO_ERROR, all_cost=1.9956183433532715, timestamp=1719239492.3938107)        [('tile_y', [-1, 1]), ('tile_x', [-1, 2])],None,10
+    No: 9   GFLOPS: 9.63/14.14      result: MeasureResult(costs=(0.0278723202,), error_no=MeasureErrorNo.NO_ERROR, all_cost=0.6925699710845947, timestamp=1719239493.2004719)       [('tile_y', [-1, 512]), ('tile_x', [-1, 32])],None,59
+    No: 10  GFLOPS: 1.06/14.14      result: MeasureResult(costs=(0.2531905212,), error_no=MeasureErrorNo.NO_ERROR, all_cost=4.3024396896362305, timestamp=1719239497.5131378)       [('tile_y', [-1, 256]), ('tile_x', [-1, 2])],None,18
 
 
 
diff --git a/docs/_sources/tutorial/autotvm_relay_x86.rst.txt b/docs/_sources/tutorial/autotvm_relay_x86.rst.txt
index 7eb1d04..20d89e5 100644
--- a/docs/_sources/tutorial/autotvm_relay_x86.rst.txt
+++ b/docs/_sources/tutorial/autotvm_relay_x86.rst.txt
Binary files differ
diff --git a/docs/_sources/tutorial/cross_compilation_and_rpc.rst.txt b/docs/_sources/tutorial/cross_compilation_and_rpc.rst.txt
index 5138f22..e08fc6b 100644
--- a/docs/_sources/tutorial/cross_compilation_and_rpc.rst.txt
+++ b/docs/_sources/tutorial/cross_compilation_and_rpc.rst.txt
@@ -274,7 +274,7 @@
 
  .. code-block:: none
 
-    1.189e-07 secs/op
+    1.203e-07 secs/op
 
 
 
diff --git a/docs/_sources/tutorial/intro_topi.rst.txt b/docs/_sources/tutorial/intro_topi.rst.txt
index 101e0e4..54c81ba 100644
--- a/docs/_sources/tutorial/intro_topi.rst.txt
+++ b/docs/_sources/tutorial/intro_topi.rst.txt
@@ -270,7 +270,7 @@
 
  .. code-block:: none
 
-    [stage(a, placeholder(a, 0x23f92f50)), stage(b, placeholder(b, 0x12cc4ec0)), stage(T_add, compute(T_add, body=[a[ax0, ax1, ax2] + b[ax1, ax2]], axis=[T.iter_var(ax0, T.Range(0, 100), "DataPar", ""), T.iter_var(ax1, T.Range(0, 10), "DataPar", ""), T.iter_var(ax2, T.Range(0, 10), "DataPar", "")], reduce_axis=[], tag=broadcast, attrs={})), stage(T_multiply, compute(T_multiply, body=[a[ax0, ax1, ax2] * b[ax1, ax2]], axis=[T.iter_var(ax0, T.Range(0, 100), "DataPar", ""), T.iter_var(ax1, T.Range(0, 10), "DataPar", ""), T.iter_var(ax2, T.Range(0, 10), "DataPar", "")], reduce_axis=[], tag=broadcast, attrs={})), stage(T_elemwise_sum, compute(T_elemwise_sum, body=[T_add[ax0, ax1, ax2] + T_multiply[ax0, ax1, ax2]], axis=[T.iter_var(ax0, T.Range(0, 100), "DataPar", ""), T.iter_var(ax1, T.Range(0, 10), "DataPar", ""), T.iter_var(ax2, T.Range(0, 10), "DataPar", "")], reduce_axis=[], tag=elemwise, attrs={})), stage(T_divide, compute(T_divide, body=[T_elemwise_sum[ax0, ax1, ax2] / T.float32(2)], axis=[T.iter_var(ax0, T.Range(0, 100), "DataPar", ""), T.iter_var(ax1, T.Range(0, 10), "DataPar", ""), T.iter_var(ax2, T.Range(0, 10), "DataPar", "")], reduce_axis=[], tag=elemwise, attrs={})), stage(T_divide_red.rf, compute(T_divide_red.rf, body=[T.reduce(T.comm_reducer(lambda x, y: x + y, [T.float32(0)]), source=[T_divide[(k0_k1_fused_k2_fused_inner + k0_k1_fused_k2_fused_outer * 1024) // 10 // 10, (k0_k1_fused_k2_fused_inner + k0_k1_fused_k2_fused_outer * 1024) // 10 % 10, (k0_k1_fused_k2_fused_inner + k0_k1_fused_k2_fused_outer * 1024) % 10]], init=[], axis=[T.iter_var(k0_k1_fused_k2_fused_outer, T.Range(0, 10), "CommReduce", "")], condition=T.likely((k0_k1_fused_k2_fused_inner + k0_k1_fused_k2_fused_outer * 1024) // 10 // 10 < 100 and (k0_k1_fused_k2_fused_inner + k0_k1_fused_k2_fused_outer * 1024) // 10 < 1000 and k0_k1_fused_k2_fused_inner + k0_k1_fused_k2_fused_outer * 1024 < 10000), value_index=0)], axis=[T.iter_var(k0_k1_fused_k2_fused_inner, T.Range(0, 1024), "DataPar", "")], reduce_axis=[T.iter_var(k0_k1_fused_k2_fused_outer, T.Range(0, 10), "CommReduce", "")], tag=, attrs={})), stage(T_divide_red, compute(T_divide_red.repl, body=[T.reduce(T.comm_reducer(lambda x, y: x + y, [T.float32(0)]), source=[T_divide_red.rf[k0_k1_fused_k2_fused_inner_v]], init=[], axis=[T.iter_var(k0_k1_fused_k2_fused_inner_v, T.Range(0, 1024), "CommReduce", "")], condition=T.bool(True), value_index=0)], axis=[], reduce_axis=[T.iter_var(k0_k1_fused_k2_fused_inner_v, T.Range(0, 1024), "CommReduce", "")], tag=, attrs={}))]
+    [stage(a, placeholder(a, 0x2977da40)), stage(b, placeholder(b, 0x2a237550)), stage(T_add, compute(T_add, body=[a[ax0, ax1, ax2] + b[ax1, ax2]], axis=[T.iter_var(ax0, T.Range(0, 100), "DataPar", ""), T.iter_var(ax1, T.Range(0, 10), "DataPar", ""), T.iter_var(ax2, T.Range(0, 10), "DataPar", "")], reduce_axis=[], tag=broadcast, attrs={})), stage(T_multiply, compute(T_multiply, body=[a[ax0, ax1, ax2] * b[ax1, ax2]], axis=[T.iter_var(ax0, T.Range(0, 100), "DataPar", ""), T.iter_var(ax1, T.Range(0, 10), "DataPar", ""), T.iter_var(ax2, T.Range(0, 10), "DataPar", "")], reduce_axis=[], tag=broadcast, attrs={})), stage(T_elemwise_sum, compute(T_elemwise_sum, body=[T_add[ax0, ax1, ax2] + T_multiply[ax0, ax1, ax2]], axis=[T.iter_var(ax0, T.Range(0, 100), "DataPar", ""), T.iter_var(ax1, T.Range(0, 10), "DataPar", ""), T.iter_var(ax2, T.Range(0, 10), "DataPar", "")], reduce_axis=[], tag=elemwise, attrs={})), stage(T_divide, compute(T_divide, body=[T_elemwise_sum[ax0, ax1, ax2] / T.float32(2)], axis=[T.iter_var(ax0, T.Range(0, 100), "DataPar", ""), T.iter_var(ax1, T.Range(0, 10), "DataPar", ""), T.iter_var(ax2, T.Range(0, 10), "DataPar", "")], reduce_axis=[], tag=elemwise, attrs={})), stage(T_divide_red.rf, compute(T_divide_red.rf, body=[T.reduce(T.comm_reducer(lambda x, y: x + y, [T.float32(0)]), source=[T_divide[(k0_k1_fused_k2_fused_inner + k0_k1_fused_k2_fused_outer * 1024) // 10 // 10, (k0_k1_fused_k2_fused_inner + k0_k1_fused_k2_fused_outer * 1024) // 10 % 10, (k0_k1_fused_k2_fused_inner + k0_k1_fused_k2_fused_outer * 1024) % 10]], init=[], axis=[T.iter_var(k0_k1_fused_k2_fused_outer, T.Range(0, 10), "CommReduce", "")], condition=T.likely((k0_k1_fused_k2_fused_inner + k0_k1_fused_k2_fused_outer * 1024) // 10 // 10 < 100 and (k0_k1_fused_k2_fused_inner + k0_k1_fused_k2_fused_outer * 1024) // 10 < 1000 and k0_k1_fused_k2_fused_inner + k0_k1_fused_k2_fused_outer * 1024 < 10000), value_index=0)], axis=[T.iter_var(k0_k1_fused_k2_fused_inner, T.Range(0, 1024), "DataPar", "")], reduce_axis=[T.iter_var(k0_k1_fused_k2_fused_outer, T.Range(0, 10), "CommReduce", "")], tag=, attrs={})), stage(T_divide_red, compute(T_divide_red.repl, body=[T.reduce(T.comm_reducer(lambda x, y: x + y, [T.float32(0)]), source=[T_divide_red.rf[k0_k1_fused_k2_fused_inner_v]], init=[], axis=[T.iter_var(k0_k1_fused_k2_fused_inner_v, T.Range(0, 1024), "CommReduce", "")], condition=T.bool(True), value_index=0)], axis=[], reduce_axis=[T.iter_var(k0_k1_fused_k2_fused_inner_v, T.Range(0, 1024), "CommReduce", "")], tag=, attrs={}))]
 
 
 
diff --git a/docs/_sources/tutorial/sg_execution_times.rst.txt b/docs/_sources/tutorial/sg_execution_times.rst.txt
index 13d30f8..ed10e23 100644
--- a/docs/_sources/tutorial/sg_execution_times.rst.txt
+++ b/docs/_sources/tutorial/sg_execution_times.rst.txt
@@ -5,31 +5,31 @@
 
 Computation times
 =================
-**16:02.175** total execution time for **tutorial** files:
+**16:12.711** total execution time for **tutorial** files:
 
 +------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_tutorial_autotvm_relay_x86.py` (``autotvm_relay_x86.py``)                 | 12:42.728 | 0.0 MB |
+| :ref:`sphx_glr_tutorial_autotvm_relay_x86.py` (``autotvm_relay_x86.py``)                 | 12:42.439 | 0.0 MB |
 +------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_tutorial_auto_scheduler_matmul_x86.py` (``auto_scheduler_matmul_x86.py``) | 01:20.519 | 0.0 MB |
+| :ref:`sphx_glr_tutorial_auto_scheduler_matmul_x86.py` (``auto_scheduler_matmul_x86.py``) | 01:21.545 | 0.0 MB |
 +------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_tutorial_tensor_expr_get_started.py` (``tensor_expr_get_started.py``)     | 00:56.898 | 0.0 MB |
+| :ref:`sphx_glr_tutorial_tensor_expr_get_started.py` (``tensor_expr_get_started.py``)     | 00:58.038 | 0.0 MB |
 +------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_tutorial_relay_quick_start.py` (``relay_quick_start.py``)                 | 00:42.937 | 0.0 MB |
+| :ref:`sphx_glr_tutorial_relay_quick_start.py` (``relay_quick_start.py``)                 | 00:44.393 | 0.0 MB |
 +------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_tutorial_autotvm_matmul_x86.py` (``autotvm_matmul_x86.py``)               | 00:16.651 | 0.0 MB |
+| :ref:`sphx_glr_tutorial_autotvm_matmul_x86.py` (``autotvm_matmul_x86.py``)               | 00:23.610 | 0.0 MB |
 +------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_tutorial_tensor_ir_blitz_course.py` (``tensor_ir_blitz_course.py``)       | 00:01.412 | 0.0 MB |
+| :ref:`sphx_glr_tutorial_tensor_ir_blitz_course.py` (``tensor_ir_blitz_course.py``)       | 00:01.473 | 0.0 MB |
 +------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_tutorial_intro_topi.py` (``intro_topi.py``)                               | 00:00.848 | 0.0 MB |
+| :ref:`sphx_glr_tutorial_intro_topi.py` (``intro_topi.py``)                               | 00:01.018 | 0.0 MB |
 +------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_tutorial_cross_compilation_and_rpc.py` (``cross_compilation_and_rpc.py``) | 00:00.181 | 0.0 MB |
+| :ref:`sphx_glr_tutorial_cross_compilation_and_rpc.py` (``cross_compilation_and_rpc.py``) | 00:00.195 | 0.0 MB |
 +------------------------------------------------------------------------------------------+-----------+--------+
 | :ref:`sphx_glr_tutorial_uma.py` (``uma.py``)                                             | 00:00.000 | 0.0 MB |
 +------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_tutorial_tvmc_python.py` (``tvmc_python.py``)                             | 00:00.000 | 0.0 MB |
-+------------------------------------------------------------------------------------------+-----------+--------+
 | :ref:`sphx_glr_tutorial_tvmc_command_line_driver.py` (``tvmc_command_line_driver.py``)   | 00:00.000 | 0.0 MB |
 +------------------------------------------------------------------------------------------+-----------+--------+
+| :ref:`sphx_glr_tutorial_tvmc_python.py` (``tvmc_python.py``)                             | 00:00.000 | 0.0 MB |
++------------------------------------------------------------------------------------------+-----------+--------+
 | :ref:`sphx_glr_tutorial_introduction.py` (``introduction.py``)                           | 00:00.000 | 0.0 MB |
 +------------------------------------------------------------------------------------------+-----------+--------+
 | :ref:`sphx_glr_tutorial_install.py` (``install.py``)                                     | 00:00.000 | 0.0 MB |
diff --git a/docs/_sources/tutorial/tensor_expr_get_started.rst.txt b/docs/_sources/tutorial/tensor_expr_get_started.rst.txt
index b9f5a96..08ed942 100644
--- a/docs/_sources/tutorial/tensor_expr_get_started.rst.txt
+++ b/docs/_sources/tutorial/tensor_expr_get_started.rst.txt
@@ -498,10 +498,10 @@
  .. code-block:: none
 
                 Operator                  Timing             Performance
-                   numpy    5.5713699998705125e-06                   1.0
-                   naive    6.418300000000001e-06     1.1520146750528455
-                parallel              7.9577e-06       1.428320143911632
-                  vector             3.88549e-05       6.974029727141269
+                   numpy    5.906879998747172e-06                    1.0
+                   naive              6.4848e-06       1.097838453020106
+                parallel              7.9832e-06       1.351508749406321
+                  vector             3.85421e-05       6.524950567503427
 
 
 
@@ -922,7 +922,7 @@
 
  .. code-block:: none
 
-    Numpy running time: 0.015505
+    Numpy running time: 0.014011
 
 
 
@@ -980,7 +980,7 @@
 
  .. code-block:: none
 
-    none: 3.260608
+    none: 3.356005
 
 
 
@@ -1080,7 +1080,7 @@
 
  .. code-block:: none
 
-    blocking: 0.277848
+    blocking: 0.283165
 
 
 
@@ -1164,7 +1164,7 @@
 
  .. code-block:: none
 
-    vectorization: 0.260909
+    vectorization: 0.262053
     # from tvm.script import ir as I
     # from tvm.script import tir as T
 
@@ -1230,7 +1230,7 @@
 
  .. code-block:: none
 
-    loop permutation: 0.108549
+    loop permutation: 0.110049
     # from tvm.script import ir as I
     # from tvm.script import tir as T
 
@@ -1321,7 +1321,7 @@
 
  .. code-block:: none
 
-    array packing: 0.102665
+    array packing: 0.101635
     # from tvm.script import ir as I
     # from tvm.script import tir as T
 
@@ -1404,7 +1404,7 @@
 
  .. code-block:: none
 
-    block caching: 0.093934
+    block caching: 0.093688
     # from tvm.script import ir as I
     # from tvm.script import tir as T
 
@@ -1478,7 +1478,7 @@
 
  .. code-block:: none
 
-    parallelization: 0.111884
+    parallelization: 0.111497
     # from tvm.script import ir as I
     # from tvm.script import tir as T
 
@@ -1548,13 +1548,13 @@
  .. code-block:: none
 
                 Operator                  Timing             Performance
-                    none            3.2606083809                     1.0
-                blocking     0.27784820379999997     0.08521360781244994
-           vectorization            0.2609092798     0.08001858834944885
-        loop permutation     0.10854901349999999      0.0332910306358343
-           array packing     0.10266533159999999     0.03148655698776744
-           block caching     0.09393448839999999    0.028808883934130107
-         parallelization     0.11188357880000002    0.034313712574436085
+                    none            3.3560048435                     1.0
+                blocking            0.2831652445     0.08437569601499297
+           vectorization            0.2620534871     0.07808495497482734
+        loop permutation            0.1100494844      0.0327918133411359
+           array packing            0.1016354327    0.030284650183640297
+           block caching     0.09368800079999999     0.02791652729031587
+         parallelization            0.1114967193     0.03322305077001001
 
 
 
diff --git a/docs/commit_hash b/docs/commit_hash
index 1564d49..6563397 100644
--- a/docs/commit_hash
+++ b/docs/commit_hash
@@ -1 +1 @@
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diff --git a/docs/genindex.html b/docs/genindex.html
index 90826d1..dd91f02 100644
--- a/docs/genindex.html
+++ b/docs/genindex.html
@@ -2837,6 +2837,8 @@
   <td style="width: 33%; vertical-align: top;"><ul>
       <li><a href="reference/api/python/target.html#tvm.target.make_compilation_config">make_compilation_config() (in module tvm.target)</a>
 </li>
+      <li><a href="reference/api/python/tir.html#tvm.tir.make_filled_simdgroup_matrix">make_filled_simdgroup_matrix() (in module tvm.tir)</a>
+</li>
       <li><a href="reference/api/python/relay/testing.html#tvm.relay.testing.make_nat_expr">make_nat_expr() (in module tvm.relay.testing)</a>
 </li>
       <li><a href="reference/api/python/relay/testing.html#tvm.relay.testing.make_nat_value">make_nat_value() (in module tvm.relay.testing)</a>
@@ -4100,10 +4102,16 @@
         <li><a href="reference/api/python/tir.html#tvm.tir.sigmoid">(in module tvm.tir)</a>
 </li>
       </ul></li>
-      <li><a href="reference/api/python/tir.html#tvm.tir.transform.Simplify">Simplify() (in module tvm.tir.transform)</a>
+      <li><a href="reference/api/python/tir.html#tvm.tir.simdgroup_load">simdgroup_load() (in module tvm.tir)</a>
+</li>
+      <li><a href="reference/api/python/tir.html#tvm.tir.simdgroup_multiply_accumulate">simdgroup_multiply_accumulate() (in module tvm.tir)</a>
+</li>
+      <li><a href="reference/api/python/tir.html#tvm.tir.simdgroup_store">simdgroup_store() (in module tvm.tir)</a>
 </li>
   </ul></td>
   <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="reference/api/python/tir.html#tvm.tir.transform.Simplify">Simplify() (in module tvm.tir.transform)</a>
+</li>
       <li><a href="reference/api/python/topi.html#tvm.topi.image.simplify">simplify() (in module tvm.topi.image)</a>
 
       <ul>
diff --git a/docs/how_to/compile_models/from_darknet.html b/docs/how_to/compile_models/from_darknet.html
index 6ec0d4a..c54535f 100644
--- a/docs/how_to/compile_models/from_darknet.html
+++ b/docs/how_to/compile_models/from_darknet.html
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diff --git a/docs/how_to/compile_models/from_keras.html b/docs/how_to/compile_models/from_keras.html
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diff --git a/docs/how_to/compile_models/from_paddle.html b/docs/how_to/compile_models/from_paddle.html
index 98ac97e..51363ba 100644
--- a/docs/how_to/compile_models/from_paddle.html
+++ b/docs/how_to/compile_models/from_paddle.html
@@ -513,7 +513,7 @@
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diff --git a/docs/how_to/compile_models/from_pytorch.html b/docs/how_to/compile_models/from_pytorch.html
index 4c1ce9a..64b8114 100644
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+++ b/docs/how_to/compile_models/from_pytorch.html
@@ -461,10 +461,10 @@
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diff --git a/docs/how_to/compile_models/from_tensorflow.html b/docs/how_to/compile_models/from_tensorflow.html
index 4661263..8ccdb13 100644
--- a/docs/how_to/compile_models/from_tensorflow.html
+++ b/docs/how_to/compile_models/from_tensorflow.html
@@ -678,7 +678,7 @@
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diff --git a/docs/how_to/compile_models/sg_execution_times.html b/docs/how_to/compile_models/sg_execution_times.html
index cf390ff..634d757 100644
--- a/docs/how_to/compile_models/sg_execution_times.html
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@@ -370,7 +370,7 @@
             
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diff --git a/docs/how_to/deploy_models/deploy_model_on_adreno.html b/docs/how_to/deploy_models/deploy_model_on_adreno.html
index 632a524..2bfee39 100644
--- a/docs/how_to/deploy_models/deploy_model_on_adreno.html
+++ b/docs/how_to/deploy_models/deploy_model_on_adreno.html
@@ -860,10 +860,10 @@
 Evaluate inference time cost...
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- 3992.3590    3991.7039    3996.1809    3990.2026      1.9953
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diff --git a/docs/how_to/deploy_models/deploy_model_on_adreno_tvmc.html b/docs/how_to/deploy_models/deploy_model_on_adreno_tvmc.html
index 3bb5fa6..1e5837b 100644
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diff --git a/docs/how_to/deploy_models/deploy_model_on_android.html b/docs/how_to/deploy_models/deploy_model_on_android.html
index 245798f..0a9b263 100644
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+++ b/docs/how_to/deploy_models/deploy_model_on_android.html
@@ -692,7 +692,7 @@
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-  13.8279      13.7426      14.3332      13.6757       0.2071
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 /venv/apache-tvm-py3.8/lib/python3.8/site-packages/torch/nn/functional.py:3912: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
   (torch.floor((input.size(i + 2).float() * torch.tensor(scale_factors[i], dtype=torch.float32)).float()))
 /venv/apache-tvm-py3.8/lib/python3.8/site-packages/torchvision/ops/boxes.py:157: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
@@ -590,7 +592,7 @@
 <div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Get 9 valid boxes
 </pre></div>
 </div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 3 minutes  21.329 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 3 minutes  27.717 seconds)</p>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-how-to-deploy-models-deploy-object-detection-pytorch-py">
 <div class="sphx-glr-download sphx-glr-download-python docutils container">
 <p><a class="reference download internal" download="" href="../../_downloads/7795da4b258c8feff986668b95ef57ad/deploy_object_detection_pytorch.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">deploy_object_detection_pytorch.py</span></code></a></p>
diff --git a/docs/how_to/deploy_models/deploy_prequantized.html b/docs/how_to/deploy_models/deploy_prequantized.html
index e142abd..18131e5 100644
--- a/docs/how_to/deploy_models/deploy_prequantized.html
+++ b/docs/how_to/deploy_models/deploy_prequantized.html
@@ -525,8 +525,8 @@
 Downloading: &quot;https://download.pytorch.org/models/mobilenet_v2-b0353104.pth&quot; to /workspace/.cache/torch/hub/checkpoints/mobilenet_v2-b0353104.pth
 
   0%|          | 0.00/13.6M [00:00&lt;?, ?B/s]
- 65%|######4   | 8.77M/13.6M [00:00&lt;00:00, 92.0MB/s]
-100%|##########| 13.6M/13.6M [00:00&lt;00:00, 67.7MB/s]
+ 59%|#####8    | 7.99M/13.6M [00:00&lt;00:00, 64.6MB/s]
+100%|##########| 13.6M/13.6M [00:00&lt;00:00, 60.0MB/s]
 </pre></div>
 </div>
 </div>
@@ -617,7 +617,7 @@
 </div>
 <div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Execution time summary:
  mean (ms)   median (ms)    max (ms)     min (ms)     std (ms)
-  85.8851      85.8682      87.5213      85.5072       0.2912
+  86.3339      86.3202      86.9115      85.7584       0.2022
 </pre></div>
 </div>
 <div class="admonition note">
@@ -656,7 +656,7 @@
 <div class="section" id="deploy-a-quantized-tflite-model">
 <h2>Deploy a quantized TFLite Model<a class="headerlink" href="#deploy-a-quantized-tflite-model" title="Permalink to this headline">¶</a></h2>
 <p>TODO</p>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes  28.654 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes  30.670 seconds)</p>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-how-to-deploy-models-deploy-prequantized-py">
 <div class="sphx-glr-download sphx-glr-download-python docutils container">
 <p><a class="reference download internal" download="" href="../../_downloads/fb8217c13f4351224c6cf3aacf1a87fc/deploy_prequantized.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">deploy_prequantized.py</span></code></a></p>
diff --git a/docs/how_to/deploy_models/deploy_prequantized_tflite.html b/docs/how_to/deploy_models/deploy_prequantized_tflite.html
index e004f25..2d4f319 100644
--- a/docs/how_to/deploy_models/deploy_prequantized_tflite.html
+++ b/docs/how_to/deploy_models/deploy_prequantized_tflite.html
@@ -610,7 +610,7 @@
 </div>
 <div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Execution time summary:
  mean (ms)   median (ms)    max (ms)     min (ms)     std (ms)
-  102.5848     102.4513     108.2271     102.0635      0.6542
+  106.9010     106.9923     107.4698     105.7611      0.3254
 </pre></div>
 </div>
 <div class="admonition note">
diff --git a/docs/how_to/deploy_models/sg_execution_times.html b/docs/how_to/deploy_models/sg_execution_times.html
index 88057a9..3b04bbd 100644
--- a/docs/how_to/deploy_models/sg_execution_times.html
+++ b/docs/how_to/deploy_models/sg_execution_times.html
@@ -370,7 +370,7 @@
             
   <div class="section" id="computation-times">
 <span id="sphx-glr-how-to-deploy-models-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Permalink to this headline">¶</a></h1>
-<p><strong>09:34.1000</strong> total execution time for <strong>how_to_deploy_models</strong> files:</p>
+<p><strong>09:49.401</strong> total execution time for <strong>how_to_deploy_models</strong> files:</p>
 <table class="docutils align-default">
 <colgroup>
 <col style="width: 86%" />
@@ -379,35 +379,35 @@
 </colgroup>
 <tbody>
 <tr class="row-odd"><td><p><a class="reference internal" href="deploy_object_detection_pytorch.html#sphx-glr-how-to-deploy-models-deploy-object-detection-pytorch-py"><span class="std std-ref">Compile PyTorch Object Detection Models</span></a> (<code class="docutils literal notranslate"><span class="pre">deploy_object_detection_pytorch.py</span></code>)</p></td>
-<td><p>03:21.329</p></td>
+<td><p>03:27.717</p></td>
 <td><p>0.0 MB</p></td>
 </tr>
 <tr class="row-even"><td><p><a class="reference internal" href="deploy_prequantized.html#sphx-glr-how-to-deploy-models-deploy-prequantized-py"><span class="std std-ref">Deploy a Framework-prequantized Model with TVM</span></a> (<code class="docutils literal notranslate"><span class="pre">deploy_prequantized.py</span></code>)</p></td>
-<td><p>01:28.654</p></td>
+<td><p>01:30.670</p></td>
 <td><p>0.0 MB</p></td>
 </tr>
 <tr class="row-odd"><td><p><a class="reference internal" href="deploy_model_on_adreno.html#sphx-glr-how-to-deploy-models-deploy-model-on-adreno-py"><span class="std std-ref">Deploy the Pretrained Model on Adreno™</span></a> (<code class="docutils literal notranslate"><span class="pre">deploy_model_on_adreno.py</span></code>)</p></td>
-<td><p>01:17.902</p></td>
+<td><p>01:18.592</p></td>
 <td><p>0.0 MB</p></td>
 </tr>
 <tr class="row-even"><td><p><a class="reference internal" href="deploy_model_on_android.html#sphx-glr-how-to-deploy-models-deploy-model-on-android-py"><span class="std std-ref">Deploy the Pretrained Model on Android</span></a> (<code class="docutils literal notranslate"><span class="pre">deploy_model_on_android.py</span></code>)</p></td>
-<td><p>00:51.056</p></td>
+<td><p>00:52.756</p></td>
 <td><p>0.0 MB</p></td>
 </tr>
 <tr class="row-odd"><td><p><a class="reference internal" href="deploy_prequantized_tflite.html#sphx-glr-how-to-deploy-models-deploy-prequantized-tflite-py"><span class="std std-ref">Deploy a Framework-prequantized Model with TVM - Part 3 (TFLite)</span></a> (<code class="docutils literal notranslate"><span class="pre">deploy_prequantized_tflite.py</span></code>)</p></td>
-<td><p>00:49.836</p></td>
+<td><p>00:51.596</p></td>
 <td><p>0.0 MB</p></td>
 </tr>
 <tr class="row-even"><td><p><a class="reference internal" href="deploy_model_on_adreno_tvmc.html#sphx-glr-how-to-deploy-models-deploy-model-on-adreno-tvmc-py"><span class="std std-ref">Deploy the Pretrained Model on Adreno™ with tvmc Interface</span></a> (<code class="docutils literal notranslate"><span class="pre">deploy_model_on_adreno_tvmc.py</span></code>)</p></td>
-<td><p>00:46.982</p></td>
+<td><p>00:48.012</p></td>
 <td><p>0.0 MB</p></td>
 </tr>
 <tr class="row-odd"><td><p><a class="reference internal" href="deploy_model_on_nano.html#sphx-glr-how-to-deploy-models-deploy-model-on-nano-py"><span class="std std-ref">Deploy the Pretrained Model on Jetson Nano</span></a> (<code class="docutils literal notranslate"><span class="pre">deploy_model_on_nano.py</span></code>)</p></td>
-<td><p>00:29.708</p></td>
+<td><p>00:30.123</p></td>
 <td><p>0.0 MB</p></td>
 </tr>
 <tr class="row-even"><td><p><a class="reference internal" href="deploy_model_on_rasp.html#sphx-glr-how-to-deploy-models-deploy-model-on-rasp-py"><span class="std std-ref">Deploy the Pretrained Model on Raspberry Pi</span></a> (<code class="docutils literal notranslate"><span class="pre">deploy_model_on_rasp.py</span></code>)</p></td>
-<td><p>00:29.526</p></td>
+<td><p>00:29.927</p></td>
 <td><p>0.0 MB</p></td>
 </tr>
 <tr class="row-odd"><td><p><a class="reference internal" href="deploy_sparse.html#sphx-glr-how-to-deploy-models-deploy-sparse-py"><span class="std std-ref">Deploy a Hugging Face Pruned Model on CPU</span></a> (<code class="docutils literal notranslate"><span class="pre">deploy_sparse.py</span></code>)</p></td>
diff --git a/docs/how_to/extend_tvm/sg_execution_times.html b/docs/how_to/extend_tvm/sg_execution_times.html
index 9160b1d..afe04c2 100644
--- a/docs/how_to/extend_tvm/sg_execution_times.html
+++ b/docs/how_to/extend_tvm/sg_execution_times.html
@@ -370,7 +370,7 @@
             
   <div class="section" id="computation-times">
 <span id="sphx-glr-how-to-extend-tvm-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Permalink to this headline">¶</a></h1>
-<p><strong>00:03.888</strong> total execution time for <strong>how_to_extend_tvm</strong> files:</p>
+<p><strong>00:04.110</strong> total execution time for <strong>how_to_extend_tvm</strong> files:</p>
 <table class="docutils align-default">
 <colgroup>
 <col style="width: 83%" />
@@ -379,11 +379,11 @@
 </colgroup>
 <tbody>
 <tr class="row-odd"><td><p><a class="reference internal" href="use_pass_instrument.html#sphx-glr-how-to-extend-tvm-use-pass-instrument-py"><span class="std std-ref">How to Use TVM Pass Instrument</span></a> (<code class="docutils literal notranslate"><span class="pre">use_pass_instrument.py</span></code>)</p></td>
-<td><p>00:02.697</p></td>
+<td><p>00:02.858</p></td>
 <td><p>0.0 MB</p></td>
 </tr>
 <tr class="row-even"><td><p><a class="reference internal" href="use_pass_infra.html#sphx-glr-how-to-extend-tvm-use-pass-infra-py"><span class="std std-ref">How to Use TVM Pass Infra</span></a> (<code class="docutils literal notranslate"><span class="pre">use_pass_infra.py</span></code>)</p></td>
-<td><p>00:01.185</p></td>
+<td><p>00:01.244</p></td>
 <td><p>0.0 MB</p></td>
 </tr>
 <tr class="row-odd"><td><p><a class="reference internal" href="low_level_custom_pass.html#sphx-glr-how-to-extend-tvm-low-level-custom-pass-py"><span class="std std-ref">Writing a Customized Pass</span></a> (<code class="docutils literal notranslate"><span class="pre">low_level_custom_pass.py</span></code>)</p></td>
diff --git a/docs/how_to/extend_tvm/use_pass_instrument.html b/docs/how_to/extend_tvm/use_pass_instrument.html
index 8452bcc..bb5ec54 100644
--- a/docs/how_to/extend_tvm/use_pass_instrument.html
+++ b/docs/how_to/extend_tvm/use_pass_instrument.html
@@ -555,10 +555,10 @@
 </pre></div>
 </div>
 <div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Printing results of timing profile...
-InferType: 24488us [24488us] (48.67%; 48.67%)
-FoldScaleAxis: 25824us [7us] (51.33%; 51.33%)
-        FoldConstant: 25818us [1751us] (51.32%; 99.97%)
-                InferType: 24067us [24067us] (47.83%; 93.22%)
+InferType: 24692us [24692us] (48.49%; 48.49%)
+FoldScaleAxis: 26231us [9us] (51.51%; 51.51%)
+        FoldConstant: 26222us [1763us] (51.49%; 99.97%)
+                InferType: 24458us [24458us] (48.03%; 93.28%)
 </pre></div>
 </div>
 </div>
@@ -580,10 +580,10 @@
 </pre></div>
 </div>
 <div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Printing results of timing profile...
-InferType: 24538us [24538us] (49.36%; 49.36%)
-FoldScaleAxis: 25177us [6us] (50.64%; 50.64%)
-        FoldConstant: 25171us [1657us] (50.63%; 99.98%)
-                InferType: 23514us [23514us] (47.30%; 93.42%)
+InferType: 24394us [24394us] (48.35%; 48.35%)
+FoldScaleAxis: 26058us [8us] (51.65%; 51.65%)
+        FoldConstant: 26050us [1803us] (51.63%; 99.97%)
+                InferType: 24247us [24247us] (48.06%; 93.08%)
 </pre></div>
 </div>
 <p>Register empty list to clear existing instruments.</p>
diff --git a/docs/how_to/optimize_operators/opt_conv_cuda.html b/docs/how_to/optimize_operators/opt_conv_cuda.html
index afd2be7..9d0242e 100644
--- a/docs/how_to/optimize_operators/opt_conv_cuda.html
+++ b/docs/how_to/optimize_operators/opt_conv_cuda.html
@@ -605,7 +605,7 @@
 <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Convolution: </span><span class="si">%f</span><span class="s2"> ms&quot;</span> <span class="o">%</span> <span class="p">(</span><span class="n">evaluator</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">w</span><span class="p">,</span> <span class="n">b</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span> <span class="o">*</span> <span class="mf">1e3</span><span class="p">))</span>
 </pre></div>
 </div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Convolution: 53.506336 ms
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Convolution: 35.825408 ms
 </pre></div>
 </div>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-how-to-optimize-operators-opt-conv-cuda-py">
diff --git a/docs/how_to/optimize_operators/opt_conv_tensorcore.html b/docs/how_to/optimize_operators/opt_conv_tensorcore.html
index 96e78b2..71058f5 100644
--- a/docs/how_to/optimize_operators/opt_conv_tensorcore.html
+++ b/docs/how_to/optimize_operators/opt_conv_tensorcore.html
@@ -887,7 +887,7 @@
     <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;conv2d with tensor core: </span><span class="si">%f</span><span class="s2"> ms&quot;</span> <span class="o">%</span> <span class="p">(</span><span class="n">evaluator</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">w</span><span class="p">,</span> <span class="n">c</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span> <span class="o">*</span> <span class="mf">1e3</span><span class="p">))</span>
 </pre></div>
 </div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>conv2d with tensor core: 11.243111 ms
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>conv2d with tensor core: 12.201779 ms
 </pre></div>
 </div>
 </div>
diff --git a/docs/how_to/optimize_operators/opt_gemm.html b/docs/how_to/optimize_operators/opt_gemm.html
index 7d8f5c8..55b27a3 100644
--- a/docs/how_to/optimize_operators/opt_gemm.html
+++ b/docs/how_to/optimize_operators/opt_gemm.html
@@ -502,8 +502,8 @@
 <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Baseline: </span><span class="si">%f</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="n">evaluator</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">,</span> <span class="n">c</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">)</span>
 </pre></div>
 </div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Numpy running time: 0.013599
-Baseline: 3.114235
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Numpy running time: 0.013816
+Baseline: 3.272670
 </pre></div>
 </div>
 <p>In TVM, we can always inspect lower level IR to debug or optimize our schedule.
@@ -562,7 +562,7 @@
 <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Opt1: </span><span class="si">%f</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="n">evaluator</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">,</span> <span class="n">c</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">)</span>
 </pre></div>
 </div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt1: 0.291226
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt1: 0.290892
 </pre></div>
 </div>
 <p>Here is the generated IR after blocking.</p>
@@ -619,7 +619,7 @@
 <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Opt2: </span><span class="si">%f</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="n">evaluator</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">,</span> <span class="n">c</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">)</span>
 </pre></div>
 </div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt2: 0.269819
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt2: 0.268008
 </pre></div>
 </div>
 <p>Here is the generated IR after vectorization.</p>
@@ -674,7 +674,7 @@
 <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Opt3: </span><span class="si">%f</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="n">evaluator</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">,</span> <span class="n">c</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">)</span>
 </pre></div>
 </div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt3: 0.115345
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt3: 0.108291
 </pre></div>
 </div>
 <p>Here is the generated IR after loop permutation.</p>
@@ -751,7 +751,7 @@
 <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Opt4: </span><span class="si">%f</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="n">evaluator</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">,</span> <span class="n">c</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">)</span>
 </pre></div>
 </div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt4: 0.105055
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt4: 0.102623
 </pre></div>
 </div>
 <p>Here is the generated IR after array packing.</p>
@@ -829,7 +829,7 @@
 <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Opt5: </span><span class="si">%f</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="n">evaluator</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">,</span> <span class="n">c</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">)</span>
 </pre></div>
 </div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt5: 0.095636
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt5: 0.093889
 </pre></div>
 </div>
 <p>Here is the generated IR after blocking.</p>
@@ -909,7 +909,7 @@
 <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Opt6: </span><span class="si">%f</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="n">opt6_time</span><span class="p">)</span>
 </pre></div>
 </div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt6: 0.114014
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opt6: 0.112948
 </pre></div>
 </div>
 <p>Here is the generated IR after parallelization.</p>
diff --git a/docs/how_to/optimize_operators/sg_execution_times.html b/docs/how_to/optimize_operators/sg_execution_times.html
index 9500e47..04751ff 100644
--- a/docs/how_to/optimize_operators/sg_execution_times.html
+++ b/docs/how_to/optimize_operators/sg_execution_times.html
@@ -370,7 +370,7 @@
             
   <div class="section" id="computation-times">
 <span id="sphx-glr-how-to-optimize-operators-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Permalink to this headline">¶</a></h1>
-<p><strong>00:31.420</strong> total execution time for <strong>how_to_optimize_operators</strong> files:</p>
+<p><strong>00:31.445</strong> total execution time for <strong>how_to_optimize_operators</strong> files:</p>
 <table class="docutils align-default">
 <colgroup>
 <col style="width: 83%" />
@@ -379,15 +379,15 @@
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-<td><p>00:28.299</p></td>
+<td><p>00:28.398</p></td>
 <td><p>0.0 MB</p></td>
 </tr>
 <tr class="row-even"><td><p><a class="reference internal" href="opt_conv_tensorcore.html#sphx-glr-how-to-optimize-operators-opt-conv-tensorcore-py"><span class="std std-ref">How to optimize convolution using TensorCores</span></a> (<code class="docutils literal notranslate"><span class="pre">opt_conv_tensorcore.py</span></code>)</p></td>
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+<td><p>00:01.939</p></td>
 <td><p>0.0 MB</p></td>
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 <tr class="row-odd"><td><p><a class="reference internal" href="opt_conv_cuda.html#sphx-glr-how-to-optimize-operators-opt-conv-cuda-py"><span class="std std-ref">How to optimize convolution on GPU</span></a> (<code class="docutils literal notranslate"><span class="pre">opt_conv_cuda.py</span></code>)</p></td>
-<td><p>00:01.175</p></td>
+<td><p>00:01.108</p></td>
 <td><p>0.0 MB</p></td>
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diff --git a/docs/how_to/tune_with_autoscheduler/sg_execution_times.html b/docs/how_to/tune_with_autoscheduler/sg_execution_times.html
index 5dd0823..8b949a0 100644
--- a/docs/how_to/tune_with_autoscheduler/sg_execution_times.html
+++ b/docs/how_to/tune_with_autoscheduler/sg_execution_times.html
@@ -370,7 +370,7 @@
             
   <div class="section" id="computation-times">
 <span id="sphx-glr-how-to-tune-with-autoscheduler-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Permalink to this headline">¶</a></h1>
-<p><strong>03:35.339</strong> total execution time for <strong>how_to_tune_with_autoscheduler</strong> files:</p>
+<p><strong>03:39.725</strong> total execution time for <strong>how_to_tune_with_autoscheduler</strong> files:</p>
 <table class="docutils align-default">
 <colgroup>
 <col style="width: 85%" />
@@ -379,27 +379,27 @@
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 <tr class="row-odd"><td><p><a class="reference internal" href="tune_network_x86.html#sphx-glr-how-to-tune-with-autoscheduler-tune-network-x86-py"><span class="std std-ref">Auto-scheduling a Neural Network for x86 CPU</span></a> (<code class="docutils literal notranslate"><span class="pre">tune_network_x86.py</span></code>)</p></td>
-<td><p>01:33.597</p></td>
+<td><p>01:34.792</p></td>
 <td><p>0.0 MB</p></td>
 </tr>
 <tr class="row-even"><td><p><a class="reference internal" href="tune_network_cuda.html#sphx-glr-how-to-tune-with-autoscheduler-tune-network-cuda-py"><span class="std std-ref">Auto-scheduling a Neural Network for NVIDIA GPU</span></a> (<code class="docutils literal notranslate"><span class="pre">tune_network_cuda.py</span></code>)</p></td>
-<td><p>01:10.747</p></td>
+<td><p>01:11.824</p></td>
 <td><p>0.0 MB</p></td>
 </tr>
 <tr class="row-odd"><td><p><a class="reference internal" href="tune_network_arm.html#sphx-glr-how-to-tune-with-autoscheduler-tune-network-arm-py"><span class="std std-ref">Auto-scheduling a Neural Network for ARM CPU</span></a> (<code class="docutils literal notranslate"><span class="pre">tune_network_arm.py</span></code>)</p></td>
-<td><p>00:19.548</p></td>
+<td><p>00:20.072</p></td>
 <td><p>0.0 MB</p></td>
 </tr>
 <tr class="row-even"><td><p><a class="reference internal" href="tune_network_mali.html#sphx-glr-how-to-tune-with-autoscheduler-tune-network-mali-py"><span class="std std-ref">Auto-scheduling a Neural Network for mali GPU</span></a> (<code class="docutils literal notranslate"><span class="pre">tune_network_mali.py</span></code>)</p></td>
-<td><p>00:16.541</p></td>
+<td><p>00:17.432</p></td>
 <td><p>0.0 MB</p></td>
 </tr>
 <tr class="row-odd"><td><p><a class="reference internal" href="tune_conv2d_layer_cuda.html#sphx-glr-how-to-tune-with-autoscheduler-tune-conv2d-layer-cuda-py"><span class="std std-ref">Auto-scheduling a Convolution Layer for GPU</span></a> (<code class="docutils literal notranslate"><span class="pre">tune_conv2d_layer_cuda.py</span></code>)</p></td>
-<td><p>00:14.807</p></td>
+<td><p>00:15.506</p></td>
 <td><p>0.0 MB</p></td>
 </tr>
 <tr class="row-even"><td><p><a class="reference internal" href="tune_sparse_x86.html#sphx-glr-how-to-tune-with-autoscheduler-tune-sparse-x86-py"><span class="std std-ref">Auto-scheduling Sparse Matrix Multiplication on CPU with Custom Sketch Rule</span></a> (<code class="docutils literal notranslate"><span class="pre">tune_sparse_x86.py</span></code>)</p></td>
-<td><p>00:00.098</p></td>
+<td><p>00:00.100</p></td>
 <td><p>0.0 MB</p></td>
 </tr>
 </tbody>
diff --git a/docs/how_to/tune_with_autoscheduler/tune_conv2d_layer_cuda.html b/docs/how_to/tune_with_autoscheduler/tune_conv2d_layer_cuda.html
index 0804f00..1785add 100644
--- a/docs/how_to/tune_with_autoscheduler/tune_conv2d_layer_cuda.html
+++ b/docs/how_to/tune_with_autoscheduler/tune_conv2d_layer_cuda.html
@@ -1042,7 +1042,7 @@
 <span class="p">)</span>
 </pre></div>
 </div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Execution time of this operator: 0.350 ms
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Execution time of this operator: 0.352 ms
 </pre></div>
 </div>
 </div>
diff --git a/docs/how_to/tune_with_autoscheduler/tune_network_cuda.html b/docs/how_to/tune_with_autoscheduler/tune_network_cuda.html
index feae794..2426b6b 100644
--- a/docs/how_to/tune_with_autoscheduler/tune_network_cuda.html
+++ b/docs/how_to/tune_with_autoscheduler/tune_network_cuda.html
@@ -933,7 +933,7 @@
 Evaluate inference time cost...
 Execution time summary:
  mean (ms)   median (ms)    max (ms)     min (ms)     std (ms)
-   3.2481       3.2516       3.2531       3.2395       0.0061
+   3.2637       3.2651       3.2659       3.2601       0.0025
 </pre></div>
 </div>
 </div>
@@ -955,7 +955,7 @@
 To use the RPC Tracker in auto-scheduler, replace the runner in <code class="code docutils literal notranslate"><span class="pre">TuningOptions</span></code>
 with <a class="reference internal" href="../../reference/api/python/auto_scheduler.html#tvm.auto_scheduler.RPCRunner" title="tvm.auto_scheduler.RPCRunner"><code class="xref any py py-class docutils literal notranslate"><span class="pre">auto_scheduler.RPCRunner</span></code></a>.</p></li>
 </ol>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes  10.747 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes  11.824 seconds)</p>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-how-to-tune-with-autoscheduler-tune-network-cuda-py">
 <div class="sphx-glr-download sphx-glr-download-python docutils container">
 <p><a class="reference download internal" download="" href="../../_downloads/eafe360d52540634c9eea0fa89e804bd/tune_network_cuda.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">tune_network_cuda.py</span></code></a></p>
diff --git a/docs/how_to/tune_with_autoscheduler/tune_network_x86.html b/docs/how_to/tune_with_autoscheduler/tune_network_x86.html
index 5410351..7c79069 100644
--- a/docs/how_to/tune_with_autoscheduler/tune_network_x86.html
+++ b/docs/how_to/tune_with_autoscheduler/tune_network_x86.html
@@ -955,7 +955,7 @@
 Evaluate inference time cost...
 Execution time summary:
  mean (ms)   median (ms)    max (ms)     min (ms)     std (ms)
-  715.0204     714.7141     716.8798     713.4673      1.4099
+  705.0103     703.5363     708.5367     702.9581      2.5046
 </pre></div>
 </div>
 </div>
@@ -977,7 +977,7 @@
 To use the RPC Tracker in auto-scheduler, replace the runner in <code class="code docutils literal notranslate"><span class="pre">TuningOptions</span></code>
 with <a class="reference internal" href="../../reference/api/python/auto_scheduler.html#tvm.auto_scheduler.RPCRunner" title="tvm.auto_scheduler.RPCRunner"><code class="xref any py py-class docutils literal notranslate"><span class="pre">auto_scheduler.RPCRunner</span></code></a>.</p></li>
 </ol>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes  33.597 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes  34.792 seconds)</p>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-how-to-tune-with-autoscheduler-tune-network-x86-py">
 <div class="sphx-glr-download sphx-glr-download-python docutils container">
 <p><a class="reference download internal" download="" href="../../_downloads/e416b94ca1090b0897c0f6e0df95b911/tune_network_x86.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">tune_network_x86.py</span></code></a></p>
diff --git a/docs/how_to/tune_with_autotvm/sg_execution_times.html b/docs/how_to/tune_with_autotvm/sg_execution_times.html
index c3a9460..312dbdc 100644
--- a/docs/how_to/tune_with_autotvm/sg_execution_times.html
+++ b/docs/how_to/tune_with_autotvm/sg_execution_times.html
@@ -370,7 +370,7 @@
             
   <div class="section" id="computation-times">
 <span id="sphx-glr-how-to-tune-with-autotvm-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Permalink to this headline">¶</a></h1>
-<p><strong>00:23.554</strong> total execution time for <strong>how_to_tune_with_autotvm</strong> files:</p>
+<p><strong>00:23.036</strong> total execution time for <strong>how_to_tune_with_autotvm</strong> files:</p>
 <table class="docutils align-default">
 <colgroup>
 <col style="width: 84%" />
@@ -379,11 +379,11 @@
 </colgroup>
 <tbody>
 <tr class="row-odd"><td><p><a class="reference internal" href="tune_conv2d_cuda.html#sphx-glr-how-to-tune-with-autotvm-tune-conv2d-cuda-py"><span class="std std-ref">Tuning High Performance Convolution on NVIDIA GPUs</span></a> (<code class="docutils literal notranslate"><span class="pre">tune_conv2d_cuda.py</span></code>)</p></td>
-<td><p>00:23.517</p></td>
+<td><p>00:22.987</p></td>
 <td><p>0.0 MB</p></td>
 </tr>
 <tr class="row-even"><td><p><a class="reference internal" href="tune_relay_x86.html#sphx-glr-how-to-tune-with-autotvm-tune-relay-x86-py"><span class="std std-ref">Auto-tuning a Convolutional Network for x86 CPU</span></a> (<code class="docutils literal notranslate"><span class="pre">tune_relay_x86.py</span></code>)</p></td>
-<td><p>00:00.020</p></td>
+<td><p>00:00.033</p></td>
 <td><p>0.0 MB</p></td>
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 <tr class="row-odd"><td><p><a class="reference internal" href="tune_relay_mobile_gpu.html#sphx-glr-how-to-tune-with-autotvm-tune-relay-mobile-gpu-py"><span class="std std-ref">Auto-tuning a Convolutional Network for Mobile GPU</span></a> (<code class="docutils literal notranslate"><span class="pre">tune_relay_mobile_gpu.py</span></code>)</p></td>
@@ -391,7 +391,7 @@
 <td><p>0.0 MB</p></td>
 </tr>
 <tr class="row-even"><td><p><a class="reference internal" href="tune_relay_arm.html#sphx-glr-how-to-tune-with-autotvm-tune-relay-arm-py"><span class="std std-ref">Auto-tuning a Convolutional Network for ARM CPU</span></a> (<code class="docutils literal notranslate"><span class="pre">tune_relay_arm.py</span></code>)</p></td>
-<td><p>00:00.005</p></td>
+<td><p>00:00.006</p></td>
 <td><p>0.0 MB</p></td>
 </tr>
 <tr class="row-odd"><td><p><a class="reference internal" href="tune_relay_cuda.html#sphx-glr-how-to-tune-with-autotvm-tune-relay-cuda-py"><span class="std std-ref">Auto-tuning a Convolutional Network for NVIDIA GPU</span></a> (<code class="docutils literal notranslate"><span class="pre">tune_relay_cuda.py</span></code>)</p></td>
diff --git a/docs/how_to/tune_with_autotvm/tune_conv2d_cuda.html b/docs/how_to/tune_with_autotvm/tune_conv2d_cuda.html
index c8ff126..68c6839 100644
--- a/docs/how_to/tune_with_autotvm/tune_conv2d_cuda.html
+++ b/docs/how_to/tune_with_autotvm/tune_conv2d_cuda.html
@@ -640,7 +640,7 @@
 
 Best config:
 ,None
-Time cost of this operator: 0.037074
+Time cost of this operator: 0.037033
 </pre></div>
 </div>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-how-to-tune-with-autotvm-tune-conv2d-cuda-py">
diff --git a/docs/how_to/work_with_microtvm/micro_autotune.html b/docs/how_to/work_with_microtvm/micro_autotune.html
index f4154aa..f2ee568 100644
--- a/docs/how_to/work_with_microtvm/micro_autotune.html
+++ b/docs/how_to/work_with_microtvm/micro_autotune.html
@@ -674,10 +674,10 @@
 <div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>########## Build without Autotuning ##########
 Node Name                                     Ops                                           Time(us)  Time(%)  Shape              Inputs  Outputs  Measurements(us)
 ---------                                     ---                                           --------  -------  -----              ------  -------  ----------------
-tvmgen_default_fused_nn_contrib_conv2d_NCHWc  tvmgen_default_fused_nn_contrib_conv2d_NCHWc  296.6     98.753   (1, 2, 10, 10, 3)  2       1        [296.6]
-tvmgen_default_fused_layout_transform_1       tvmgen_default_fused_layout_transform_1       2.799     0.932    (1, 6, 10, 10)     1       1        [2.799]
-tvmgen_default_fused_layout_transform         tvmgen_default_fused_layout_transform         0.947     0.315    (1, 1, 10, 10, 3)  1       1        [0.947]
-Total_time                                    -                                             300.346   -        -                  -       -        -
+tvmgen_default_fused_nn_contrib_conv2d_NCHWc  tvmgen_default_fused_nn_contrib_conv2d_NCHWc  296.4     98.759   (1, 2, 10, 10, 3)  2       1        [296.4]
+tvmgen_default_fused_layout_transform_1       tvmgen_default_fused_layout_transform_1       2.786     0.928    (1, 6, 10, 10)     1       1        [2.786]
+tvmgen_default_fused_layout_transform         tvmgen_default_fused_layout_transform         0.938     0.313    (1, 1, 10, 10, 3)  1       1        [0.938]
+Total_time                                    -                                             300.124   -        -                  -       -        -
 </pre></div>
 </div>
 </div>
@@ -729,13 +729,13 @@
 <div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>########## Build with Autotuning ##########
 Node Name                                     Ops                                           Time(us)  Time(%)  Shape              Inputs  Outputs  Measurements(us)
 ---------                                     ---                                           --------  -------  -----              ------  -------  ----------------
-tvmgen_default_fused_nn_contrib_conv2d_NCHWc  tvmgen_default_fused_nn_contrib_conv2d_NCHWc  100.5     97.396   (1, 6, 10, 10, 1)  2       1        [100.5]
-tvmgen_default_fused_layout_transform_1       tvmgen_default_fused_layout_transform_1       1.752     1.698    (1, 6, 10, 10)     1       1        [1.752]
-tvmgen_default_fused_layout_transform         tvmgen_default_fused_layout_transform         0.935     0.906    (1, 3, 10, 10, 1)  1       1        [0.935]
-Total_time                                    -                                             103.187   -        -                  -       -        -
+tvmgen_default_fused_nn_contrib_conv2d_NCHWc  tvmgen_default_fused_nn_contrib_conv2d_NCHWc  100.5     97.541   (1, 6, 10, 10, 1)  2       1        [100.5]
+tvmgen_default_fused_layout_transform_1       tvmgen_default_fused_layout_transform_1       1.707     1.657    (1, 6, 10, 10)     1       1        [1.707]
+tvmgen_default_fused_layout_transform         tvmgen_default_fused_layout_transform         0.827     0.802    (1, 3, 10, 10, 1)  1       1        [0.827]
+Total_time                                    -                                             103.033   -        -                  -       -        -
 </pre></div>
 </div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes  21.930 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes  24.297 seconds)</p>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-how-to-work-with-microtvm-micro-autotune-py">
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diff --git a/docs/reference/api/doxygen/namespacemembers_func_s.html b/docs/reference/api/doxygen/namespacemembers_func_s.html
index ac70e95..13f0baf 100644
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+++ b/docs/reference/api/doxygen/namespacemembers_func_s.html
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+: <a class="el" href="namespacetvm_1_1runtime_1_1profiling.html#a0db7b8aaaf50308b175b79c8fee98f13">tvm::runtime::profiling</a>
 </li>
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 </li>
+<li>simdgroup_load()
+: <a class="el" href="namespacetvm_1_1tir_1_1builtin.html#a71144e4b9854f302e5ed9895032b39b3">tvm::tir::builtin</a>
+</li>
+<li>simdgroup_multiply_accumulate()
+: <a class="el" href="namespacetvm_1_1tir_1_1builtin.html#a63d5e1b5ccfa5e46dadb5a9433b861b7">tvm::tir::builtin</a>
+</li>
+<li>simdgroup_store()
+: <a class="el" href="namespacetvm_1_1tir_1_1builtin.html#a1bfea12cd063c33084e3c7c07c4348a9">tvm::tir::builtin</a>
+</li>
 <li>Simplify()
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diff --git a/docs/reference/api/doxygen/namespacemembers_m.html b/docs/reference/api/doxygen/namespacemembers_m.html
index 867e77f..69fa877 100644
--- a/docs/reference/api/doxygen/namespacemembers_m.html
+++ b/docs/reference/api/doxygen/namespacemembers_m.html
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 </li>
+<li>make_filled_simdgroup_matrix()
+: <a class="el" href="namespacetvm_1_1tir_1_1builtin.html#afb5279d0a2ce8c3c73de80dc2b71fa77">tvm::tir::builtin</a>
+</li>
 <li>make_inplace_array_object()
 : <a class="el" href="namespacetvm_1_1runtime.html#a0537c9d197068a02c26cd702ab42f6ff">tvm::runtime</a>
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 <li>make_object()
-: <a class="el" href="namespacetvm_1_1runtime.html#af55276b6524dbaf78e3eaba39ee9ce16">tvm::runtime</a>
+: <a class="el" href="namespacetvm_1_1runtime.html#a3189d7506cfd1acacd3cdfc04d399a03">tvm::runtime</a>
 </li>
 <li>make_zero()
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diff --git a/docs/reference/api/doxygen/namespacemembers_s.html b/docs/reference/api/doxygen/namespacemembers_s.html
index ea8d311..60a2f0b 100644
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+++ b/docs/reference/api/doxygen/namespacemembers_s.html
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 <li>SignType
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 </li>
+<li>simdgroup_load()
+: <a class="el" href="namespacetvm_1_1tir_1_1builtin.html#a71144e4b9854f302e5ed9895032b39b3">tvm::tir::builtin</a>
+</li>
+<li>simdgroup_multiply_accumulate()
+: <a class="el" href="namespacetvm_1_1tir_1_1builtin.html#a63d5e1b5ccfa5e46dadb5a9433b861b7">tvm::tir::builtin</a>
+</li>
+<li>simdgroup_store()
+: <a class="el" href="namespacetvm_1_1tir_1_1builtin.html#a1bfea12cd063c33084e3c7c07c4348a9">tvm::tir::builtin</a>
+</li>
 <li>Simplify()
 : <a class="el" href="namespacetvm_1_1tir_1_1transform.html#a817801e8c9488f712804d2d0b821acf0">tvm::tir::transform</a>
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 </li>
 <li>sub()
-: <a class="el" href="namespacetvm.html#a269b014ef11118b07bae6efb0300ba6f">tvm</a>
+: <a class="el" href="namespacetvm.html#af2d75a528d344c6cfcf8b726a6abb7cc">tvm</a>
 </li>
 <li>SubspaceDivide()
 : <a class="el" href="namespacetvm_1_1arith.html#a07096c818b466b42171bc93c1784cdf8">tvm::arith</a>
diff --git a/docs/reference/api/doxygen/namespacetvm_1_1tir_1_1builtin.html b/docs/reference/api/doxygen/namespacetvm_1_1tir_1_1builtin.html
index 242bec2..cad0755 100644
--- a/docs/reference/api/doxygen/namespacetvm_1_1tir_1_1builtin.html
+++ b/docs/reference/api/doxygen/namespacetvm_1_1tir_1_1builtin.html
@@ -310,8 +310,20 @@
 <tr class="memdesc:a772fb68f083e71e635c50bb503903f22"><td class="mdescLeft">&#160;</td><td class="mdescRight">tvm intrinsic for storing the result of PTX MMA into a destination pointer. <a class="el" href="classtvm_1_1tir_1_1For.html" title="Managed reference to ForNode.">For</a> example, if each thread in a warp of size 32 has 4 elements from the result of m16xn8xk16 MMA in its registers, this intrinsic can be used to store the result in a 16x8 region in shared or global memory.  <a href="namespacetvm_1_1tir_1_1builtin.html#a772fb68f083e71e635c50bb503903f22">More...</a><br /></td></tr>
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 <tr class="memitem:a307667c449c54cef747d781771f79bab"><td class="memItemLeft" align="right" valign="top">const <a class="el" href="classtvm_1_1Op.html">Op</a> &amp;&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="namespacetvm_1_1tir_1_1builtin.html#a307667c449c54cef747d781771f79bab">mma_fill</a> ()</td></tr>
-<tr class="memdesc:a307667c449c54cef747d781771f79bab"><td class="mdescLeft">&#160;</td><td class="mdescRight">tvm intrinsic for zero-initalizing an MMA accumulation registor. <a class="el" href="classtvm_1_1tir_1_1For.html" title="Managed reference to ForNode.">For</a> example, if each thread in a warp of size 32 has 8 elements from the A matrix in m16xn8xk16 MMA in its registers, this intrinsic can be used to zero-initialize its 4 accumulation registers.  <a href="namespacetvm_1_1tir_1_1builtin.html#a307667c449c54cef747d781771f79bab">More...</a><br /></td></tr>
+<tr class="memdesc:a307667c449c54cef747d781771f79bab"><td class="mdescLeft">&#160;</td><td class="mdescRight">tvm intrinsic for zero-initializing an MMA accumulation register. <a class="el" href="classtvm_1_1tir_1_1For.html" title="Managed reference to ForNode.">For</a> example, if each thread in a warp of size 32 has 8 elements from the A matrix in m16xn8xk16 MMA in its registers, this intrinsic can be used to zero-initialize its 4 accumulation registers.  <a href="namespacetvm_1_1tir_1_1builtin.html#a307667c449c54cef747d781771f79bab">More...</a><br /></td></tr>
 <tr class="separator:a307667c449c54cef747d781771f79bab"><td class="memSeparator" colspan="2">&#160;</td></tr>
+<tr class="memitem:afb5279d0a2ce8c3c73de80dc2b71fa77"><td class="memItemLeft" align="right" valign="top">const <a class="el" href="classtvm_1_1Op.html">Op</a> &amp;&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="namespacetvm_1_1tir_1_1builtin.html#afb5279d0a2ce8c3c73de80dc2b71fa77">make_filled_simdgroup_matrix</a> ()</td></tr>
+<tr class="memdesc:afb5279d0a2ce8c3c73de80dc2b71fa77"><td class="mdescLeft">&#160;</td><td class="mdescRight">tvm intrinsic for initializing and simdgroup with given value.  <a href="namespacetvm_1_1tir_1_1builtin.html#afb5279d0a2ce8c3c73de80dc2b71fa77">More...</a><br /></td></tr>
+<tr class="separator:afb5279d0a2ce8c3c73de80dc2b71fa77"><td class="memSeparator" colspan="2">&#160;</td></tr>
+<tr class="memitem:a71144e4b9854f302e5ed9895032b39b3"><td class="memItemLeft" align="right" valign="top">const <a class="el" href="classtvm_1_1Op.html">Op</a> &amp;&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="namespacetvm_1_1tir_1_1builtin.html#a71144e4b9854f302e5ed9895032b39b3">simdgroup_load</a> ()</td></tr>
+<tr class="memdesc:a71144e4b9854f302e5ed9895032b39b3"><td class="mdescLeft">&#160;</td><td class="mdescRight">tvm intrinsic for loading data from device memory or threadgroup memory to simdgroup.  <a href="namespacetvm_1_1tir_1_1builtin.html#a71144e4b9854f302e5ed9895032b39b3">More...</a><br /></td></tr>
+<tr class="separator:a71144e4b9854f302e5ed9895032b39b3"><td class="memSeparator" colspan="2">&#160;</td></tr>
+<tr class="memitem:a1bfea12cd063c33084e3c7c07c4348a9"><td class="memItemLeft" align="right" valign="top">const <a class="el" href="classtvm_1_1Op.html">Op</a> &amp;&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="namespacetvm_1_1tir_1_1builtin.html#a1bfea12cd063c33084e3c7c07c4348a9">simdgroup_store</a> ()</td></tr>
+<tr class="memdesc:a1bfea12cd063c33084e3c7c07c4348a9"><td class="mdescLeft">&#160;</td><td class="mdescRight">tvm intrinsic for storing data from simdgroup to device memory or threadgroup memory.  <a href="namespacetvm_1_1tir_1_1builtin.html#a1bfea12cd063c33084e3c7c07c4348a9">More...</a><br /></td></tr>
+<tr class="separator:a1bfea12cd063c33084e3c7c07c4348a9"><td class="memSeparator" colspan="2">&#160;</td></tr>
+<tr class="memitem:a63d5e1b5ccfa5e46dadb5a9433b861b7"><td class="memItemLeft" align="right" valign="top">const <a class="el" href="classtvm_1_1Op.html">Op</a> &amp;&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="namespacetvm_1_1tir_1_1builtin.html#a63d5e1b5ccfa5e46dadb5a9433b861b7">simdgroup_multiply_accumulate</a> ()</td></tr>
+<tr class="memdesc:a63d5e1b5ccfa5e46dadb5a9433b861b7"><td class="mdescLeft">&#160;</td><td class="mdescRight">tvm intrinsic for multiply and accumulate two matrices in simdgroup  <a href="namespacetvm_1_1tir_1_1builtin.html#a63d5e1b5ccfa5e46dadb5a9433b861b7">More...</a><br /></td></tr>
+<tr class="separator:a63d5e1b5ccfa5e46dadb5a9433b861b7"><td class="memSeparator" colspan="2">&#160;</td></tr>
 <tr class="memitem:a45bf65ca7ca01d2016e0b609117d7e25"><td class="memItemLeft" align="right" valign="top">const <a class="el" href="classtvm_1_1Op.html">Op</a> &amp;&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="namespacetvm_1_1tir_1_1builtin.html#a45bf65ca7ca01d2016e0b609117d7e25">vectorhigh</a> ()</td></tr>
 <tr class="memdesc:a45bf65ca7ca01d2016e0b609117d7e25"><td class="mdescLeft">&#160;</td><td class="mdescRight">Get the high level half of the vector.  <a href="namespacetvm_1_1tir_1_1builtin.html#a45bf65ca7ca01d2016e0b609117d7e25">More...</a><br /></td></tr>
 <tr class="separator:a45bf65ca7ca01d2016e0b609117d7e25"><td class="memSeparator" colspan="2">&#160;</td></tr>
@@ -1017,6 +1029,27 @@
 
 </div>
 </div>
+<a id="afb5279d0a2ce8c3c73de80dc2b71fa77"></a>
+<h2 class="memtitle"><span class="permalink"><a href="#afb5279d0a2ce8c3c73de80dc2b71fa77">&#9670;&nbsp;</a></span>make_filled_simdgroup_matrix()</h2>
+
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">const <a class="el" href="classtvm_1_1Op.html">Op</a>&amp; tvm::tir::builtin::make_filled_simdgroup_matrix </td>
+          <td>(</td>
+          <td class="paramname"></td><td>)</td>
+          <td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+<p>tvm intrinsic for initializing and simdgroup with given value. </p>
+<dl class="section note"><dt>Note</dt><dd>only 8x8 shape is supported by Metal Spec and TVM, but we still keep shape as params, keeping the similar interface with Metal Spec.</dd></dl>
+<p>void make_filled_simdgroup_matrix(<a class="el" href="classtvm_1_1tir_1_1Var.html" title="a named variable in TIR">Var</a> d, <a class="el" href="classtvm_1_1PrimExpr.html" title="Reference to PrimExprNode.">PrimExpr</a> index, <a class="el" href="classtvm_1_1PrimExpr.html" title="Reference to PrimExprNode.">PrimExpr</a> value, int col = 8, int row = 8); </p>
+
+</div>
+</div>
 <a id="a307667c449c54cef747d781771f79bab"></a>
 <h2 class="memtitle"><span class="permalink"><a href="#a307667c449c54cef747d781771f79bab">&#9670;&nbsp;</a></span>mma_fill()</h2>
 
@@ -1032,7 +1065,7 @@
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-<p>tvm intrinsic for zero-initalizing an MMA accumulation registor. <a class="el" href="classtvm_1_1tir_1_1For.html" title="Managed reference to ForNode.">For</a> example, if each thread in a warp of size 32 has 8 elements from the A matrix in m16xn8xk16 MMA in its registers, this intrinsic can be used to zero-initialize its 4 accumulation registers. </p>
+<p>tvm intrinsic for zero-initializing an MMA accumulation register. <a class="el" href="classtvm_1_1tir_1_1For.html" title="Managed reference to ForNode.">For</a> example, if each thread in a warp of size 32 has 8 elements from the A matrix in m16xn8xk16 MMA in its registers, this intrinsic can be used to zero-initialize its 4 accumulation registers. </p>
 <p>There is no real PTX instruction that does that, but we introduce this intrinsic for the same reason as mma_store above.</p>
 <p>void mma_fill(IntImm local_size, Var local_ptr, Expr offset); </p>
 
@@ -1467,6 +1500,69 @@
 
 </div>
 </div>
+<a id="a71144e4b9854f302e5ed9895032b39b3"></a>
+<h2 class="memtitle"><span class="permalink"><a href="#a71144e4b9854f302e5ed9895032b39b3">&#9670;&nbsp;</a></span>simdgroup_load()</h2>
+
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">const <a class="el" href="classtvm_1_1Op.html">Op</a>&amp; tvm::tir::builtin::simdgroup_load </td>
+          <td>(</td>
+          <td class="paramname"></td><td>)</td>
+          <td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+<p>tvm intrinsic for loading data from device memory or threadgroup memory to simdgroup. </p>
+<dl class="section note"><dt>Note</dt><dd>only 8x8 shape is supported by Metal Spec and TVM, but we still keep shape as params, keeping the similar interface with Metal Spec.</dd></dl>
+<p>void simdgroup_load(<a class="el" href="classtvm_1_1tir_1_1Var.html" title="a named variable in TIR">Var</a> d, <a class="el" href="classtvm_1_1PrimExpr.html" title="Reference to PrimExprNode.">PrimExpr</a> index, <a class="el" href="classtvm_1_1PrimExpr.html" title="Reference to PrimExprNode.">PrimExpr</a> ptr, <a class="el" href="classtvm_1_1PrimExpr.html" title="Reference to PrimExprNode.">PrimExpr</a> stride, int col = 8, int row = 8, bool transpose_matrix = false); </p>
+
+</div>
+</div>
+<a id="a63d5e1b5ccfa5e46dadb5a9433b861b7"></a>
+<h2 class="memtitle"><span class="permalink"><a href="#a63d5e1b5ccfa5e46dadb5a9433b861b7">&#9670;&nbsp;</a></span>simdgroup_multiply_accumulate()</h2>
+
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">const <a class="el" href="classtvm_1_1Op.html">Op</a>&amp; tvm::tir::builtin::simdgroup_multiply_accumulate </td>
+          <td>(</td>
+          <td class="paramname"></td><td>)</td>
+          <td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+<p>tvm intrinsic for multiply and accumulate two matrices in simdgroup </p>
+<dl class="section note"><dt>Note</dt><dd>only 8x8 shape is supported by Metal Spec and TVM, but we still keep shape as params, keeping the similar interface with Metal Spec.</dd></dl>
+<p>void simdgroup_mma(<a class="el" href="classtvm_1_1tir_1_1Var.html" title="a named variable in TIR">Var</a> d, <a class="el" href="classtvm_1_1PrimExpr.html" title="Reference to PrimExprNode.">PrimExpr</a> index_d, <a class="el" href="classtvm_1_1tir_1_1Var.html" title="a named variable in TIR">Var</a> a, <a class="el" href="classtvm_1_1PrimExpr.html" title="Reference to PrimExprNode.">PrimExpr</a> index_a, <a class="el" href="classtvm_1_1tir_1_1Var.html" title="a named variable in TIR">Var</a> b, <a class="el" href="classtvm_1_1PrimExpr.html" title="Reference to PrimExprNode.">PrimExpr</a> index_b, <a class="el" href="classtvm_1_1tir_1_1Var.html" title="a named variable in TIR">Var</a> c, <a class="el" href="classtvm_1_1PrimExpr.html" title="Reference to PrimExprNode.">PrimExpr</a> index_c); </p>
+
+</div>
+</div>
+<a id="a1bfea12cd063c33084e3c7c07c4348a9"></a>
+<h2 class="memtitle"><span class="permalink"><a href="#a1bfea12cd063c33084e3c7c07c4348a9">&#9670;&nbsp;</a></span>simdgroup_store()</h2>
+
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">const <a class="el" href="classtvm_1_1Op.html">Op</a>&amp; tvm::tir::builtin::simdgroup_store </td>
+          <td>(</td>
+          <td class="paramname"></td><td>)</td>
+          <td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+<p>tvm intrinsic for storing data from simdgroup to device memory or threadgroup memory. </p>
+<dl class="section note"><dt>Note</dt><dd>only 8x8 shape is supported by Metal Spec and TVM, but we still keep shape as params, keeping the similar interface with Metal Spec.</dd></dl>
+<p>void simdgroup_store(<a class="el" href="classtvm_1_1tir_1_1Var.html" title="a named variable in TIR">Var</a> d, <a class="el" href="classtvm_1_1PrimExpr.html" title="Reference to PrimExprNode.">PrimExpr</a> index, <a class="el" href="classtvm_1_1PrimExpr.html" title="Reference to PrimExprNode.">PrimExpr</a> ptr, <a class="el" href="classtvm_1_1PrimExpr.html" title="Reference to PrimExprNode.">PrimExpr</a> stride, int col = 8, int row = 8, bool transpose_matrix = false); </p>
+
+</div>
+</div>
 <a id="aa63c16281d1010a82a06eb771268b4e2"></a>
 <h2 class="memtitle"><span class="permalink"><a href="#aa63c16281d1010a82a06eb771268b4e2">&#9670;&nbsp;</a></span>start_profile_intrinsic()</h2>
 
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