commit	bbe20bb347da90909c2ae9d345c2efb3481a5abd	[log] [tgz]
author	Hua Jiang <huaj@xilinx.com>	Wed Feb 26 15:52:28 2020 -0800
committer	GitHub <noreply@github.com>	Wed Feb 26 15:52:28 2020 -0800
tree	7fa622264a624337ac6333003c60b7d888177aba
parent	436850fe7ec6576b28b7148cf9b6548fd6ca3f7d [diff]

[VTA] YoloV3 Support (#4887)

* [VTA] YoloV3 Support

Issue:
YoloV3 use some operator and logic that not get good support by
existing vta logic, like nn.pad, upsample, and 255 output channel.

Solution:
add related logic to let darknet YoloV3 can running on VTA

* Fix small(0, or 1 heigh/width) detect frame issue.

* add yolov3-tiny turtorial

* add os import

* address review comments.

* rename tutorial file with a short name.

* rename deploy_vision_on_vta.py into deploy_classification.py.

* address review comment, fix plint eror in deploy_detection.py

python/vta/top/graphpack.py[diff]
tutorials/frontend/deploy_classification.py[Renamed from tutorials/frontend/deploy_vision_on_vta.py - diff]
tutorials/frontend/deploy_detection.py[Added - diff]

3 files changed

tree: 7fa622264a624337ac6333003c60b7d888177aba

README.md

VTA: Open, Modular, Deep Learning Accelerator Stack

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:

Generic, modular, open-source hardware
- Streamlined workflow to deploy to FPGAs.
- Simulator support to prototype compilation passes on regular workstations.
Driver and JIT runtime for both simulator and FPGA hardware back-end.
End-to-end TVM stack integration
- Direct optimization and deployment of models from deep learning frameworks via TVM.
- Customized and extensible TVM compiler back-end.
- Flexible RPC support to ease deployment, and program FPGAs with the convenience of Python.

Learn more about VTA here.