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apache / tvm-vta / 6e0dbeed3209a43ab621fdb9eea0687e3b44611d
commit6e0dbeed3209a43ab621fdb9eea0687e3b44611d[log] [tgz]
authorBenjamin Tu <tu.benjamin1115@gmail.com>Thu Oct 10 13:37:05 2019 -0700
committerThierry Moreau <moreau@uw.edu>Thu Oct 10 13:37:05 2019 -0700
tree6804e4750ab6b4eee4db717ed75a157dc34dffc1
parent94354d65dcb27a47477c32178020d501daeff08c [diff]
[VTA][TSIM] Serial GEMM Application Added (#4082)

* app init push

* fix on readme

* change name, add bit serial explanantion

* rm serialLoadMM, change doc

* syntax change for readme

* add parallel test functionality

* fix readme

* add python doc

* syntax
15 files changed
tree: 6804e4750ab6b4eee4db717ed75a157dc34dffc1
  1. apps/
  2. config/
  3. hardware/
  4. include/
  5. python/
  6. scripts/
  7. src/
  8. tests/
  9. tutorials/
  10. README.md
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.