VTA: Deep Learning Accelerator Stack

The Versatile Tensor Accelerator (VTA) is an open, generic, and customizable deep learning accelerator with a complete TVM-based compiler stack. We designed VTA to expose the most salient and common characteristics of mainstream deep learning accelerators. Together TVM and VTA form an end-to-end hardware-software deep learning system stack that includes hardware design, drivers, a JIT runtime, and an optimizing compiler stack based on TVM.

http://raw.githubusercontent.com/uwsaml/web-data/master/vta/blogpost/vta_overview.png

VTA has the following key features:

  • Generic, modular, open-source hardware.
  • Streamlined workflow to deploy to FPGAs.
  • Simulator support to prototype compilation passes on regular workstations.
  • Pynq-based driver and JIT runtime for both simulated and FPGA hardware back-end.
  • End to end TVM stack integration.

This page contains links to all the resources related to VTA:

Literature