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apache / tvm-vta / 9eff1d24629687ad7859b6541c7da0e376420f6f
commit9eff1d24629687ad7859b6541c7da0e376420f6f[log] [tgz]
authorLiangfu Chen <liangfu.chen@harman.com>Tue Aug 27 00:36:23 2019 +0800
committerThierry Moreau <moreau@uw.edu>Mon Aug 26 09:36:23 2019 -0700
tree95029edae5100fd15e9725403ee058022983cde8
parent1a207bfd4fc929a9e9c7f608161c1f0c481da8a7 [diff]
[VTA][TSIM] Introduce Virtual Memory for TSIM Driver (#3686)

* initial virtual memory;

* initial integration;

* include the header file in cmake;

* implement allocation with virtual to logical address mapping;

* virtual memory for tsim_driver;

* implement the missing memory release function;

* readability improvement;

* readability improvement;

* address review comments;

* improved robustness in virtual memory allocation;

* remove VTA_TSIM_USE_VIRTUAL_MEMORY macro and use virtual memory for tsim by default;

* link tvm against vta library;

* merge with master

* build virtual memory system without linking tvm against vta;

* minor change;

* reuse VTA_PAGE_BYTES;

* using DRAM class from sim_driver as VirtualMemoryManager;

* satisfy linter;

* add comments in code;

* undo changes to Makefile

* undo changes to Makefile

* retrigger ci;

* retrigger ci;

* directly call into VirtualMemoryManager::Global()
5 files changed
tree: 95029edae5100fd15e9725403ee058022983cde8
  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.