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apache / tvm-vta / 453d0c87b555afcce8797b8a63e155aa8f92a020
commit453d0c87b555afcce8797b8a63e155aa8f92a020[log] [tgz]
authorTianqi Chen <tqchen@users.noreply.github.com>Sun Jan 05 21:39:33 2020 -0800
committerGitHub <noreply@github.com>Sun Jan 05 21:39:33 2020 -0800
treef49ee12b2295b0592923dae9b1e02f8ca1d56b83
parent80f518a57bd9a1b59140d750413a7fb0c94a21a2 [diff]
[REFACTOR][IR] Introduce SeqStmt to replace ir::Block (#4627)

* [REFACTOR][IR] Introduce SeqStmt to replace Block

ir::Block was used to represent a sequence of Stmts in the original low-level IR.
The nested ir::Block structure is not really friendly for recursive visits,
especially when the statements are unrolled.

This PR introduce a SeqStmt that directly stores a sequence of statements in an Array container.
The new SeqStmt will be used as a replacement of the original Block structure.

* [REFACTOR] Migrate use of Block to SeqStmt.

* [REFACTOR] Remove Block

* Add more comments per yizhi's comment
1 file changed
tree: f49ee12b2295b0592923dae9b1e02f8ca1d56b83
  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.