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.