VTA: Versatile Tensor Accelerator

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.

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:

• VTA Installation Guide
• VTA Design and Developer Guide
• VTA Tutorials

Literature

• Read the VTA release blog post.

• Read the VTA tech report: An Open Hardware Software Stack for Deep Learning.