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. .. image:: http://raw.githubusercontent.com/uwsaml/web-data/master/vta/blogpost/vta_overview.png :align: center :width: 60% 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: .. toctree:: :maxdepth: 1 install dev/index tutorials/index Literature ---------- - Read the VTA `release blog post`_. - Read the VTA tech report: `An Open Hardware Software Stack for Deep Learning`_. .. _release blog post: https://tvm.ai/2018/07/12/vta-release-announcement.html .. _An Open Hardware Software Stack for Deep Learning: https://arxiv.org/abs/1807.04188