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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.apache.org/2018/07/12/vta-release-announcement
.. _An Open Hardware Software Stack for Deep Learning: https://arxiv.org/abs/1807.04188