# tvm-vta

**Repository Path**: cavin_sun/tvm-vta

## Basic Information

- **Project Name**: tvm-vta
- **Description**: VTA Hardware Design Stack
clone from https://github.com/apache/tvm-vta.git
- **Primary Language**: Unknown
- **License**: Apache-2.0
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2021-02-24
- **Last Updated**: 2022-02-17

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

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VTA Hardware Design Stack
=========================
[![Build Status](https://ci.tlcpack.ai/job/tvm-vta/job/main/badge/icon)](https://ci.tlcpack.ai/job/tvm-vta/job/main/)

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.