commit | c18a22475d8ccb5447da4591c709d347687fb2b8 | [log] [tgz] |
---|---|---|
author | Abhijit Davare <abhijit.davare@intel.com> | Tue Jun 22 18:03:09 2021 -0700 |
committer | GitHub <noreply@github.com> | Wed Jun 23 09:03:09 2021 +0800 |
tree | dee39349a9bea3082da0c8f27ebeed4d83986cab | |
parent | 1954ff58264384de8241cc155ffe33829f0e616a [diff] |
Chisel Pipelined GEMM (#30) * Reset to 644 file permissions * Add json files to src/test/resources for testing * Add new TensorGemmPipelinedSplit module and rename existing TensorGemm to TensorGemmOrig * Tests for TensorGemmPipelinedSplit, TensorGemmOrig, and associated submodules * Add jackson plugin dependency and stricter Scala checks * Remove debug prints * Rename x.json and y.json to gemm_1uop_overflow_offset.json and gemm_2uop_overflow_cascaded.json respectively * All occurrences of '\( ' replaced with '\(' * Add linting rule to flag spaces after lparen characters * Remove comment * Rename TensorGemmOrig to TensorGemmSimple
VTA (versatile tensor accelerator) is an open-source deep learning accelerator complemented with an end-to-end TVM-based compiler stack.
The key features of VTA include: