<!--- Licensed to the Apache Software Foundation (ASF) under one --> <!--- or more contributor license agreements. See the NOTICE file --> <!--- distributed with this work for additional information --> <!--- regarding copyright ownership. The ASF licenses this file --> <!--- to you under the Apache License, Version 2.0 (the --> <!--- "License"); you may not use this file except in compliance --> <!--- with the License. You may obtain a copy of the License at --> <!--- http://www.apache.org/licenses/LICENSE-2.0 --> <!--- Unless required by applicable law or agreed to in writing, --> <!--- software distributed under the License is distributed on an --> <!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY --> <!--- KIND, either express or implied. See the License for the --> <!--- specific language governing permissions and limitations --> <!--- under the License. --> VTA: Open, Modular, Deep Learning Accelerator Stack =================================================== 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. Learn more about VTA [here](https://docs.tvm.ai/vta/index.html).