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.