From d7a8aff81c955080739f438358a02cef32163cf8 Mon Sep 17 00:00:00 2001 From: Yaman Umuroglu <yamanu@xilinx.com> Date: Tue, 1 Oct 2019 22:58:15 +0200 Subject: [PATCH] Update and rename README.rst to README.md --- README.md | 20 ++++++++++++++++++++ README.rst | 19 ------------------- 2 files changed, 20 insertions(+), 19 deletions(-) create mode 100644 README.md delete mode 100644 README.rst diff --git a/README.md b/README.md new file mode 100644 index 000000000..064f6725e --- /dev/null +++ b/README.md @@ -0,0 +1,20 @@ +# FINN + +Fast, Scalable Quantized Neural Network Inference on FPGAs + +[](https://gitter.im/xilinx-finn/community?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge) + + +## Description + + +FINN is an experimental framework from Xilinx Research Labs to explore deep neural network inference on FPGAs. It specifically targets quantized neural networks, with emphasis on generating dataflow-style architectures customized for each network. +For more information, please visit the [project page](https://xilinx.github.io/finn/). + +A new, more modular version of FINN is currently under development on GitHub, and we welcome contributions from the community! +Stay tuned for more updates. + +## Old version + +We previously released an early-stage prototype of a toolflow that took in Caffe-HWGQ binarized network descriptions and produced . You can find it in the [v0.1](https://github.com/Xilinx/finn/tree/v0.1) branch in this repository. +Please be aware that this version is deprecated and unsupported, and the master branch does not share history with that branch so it should be treated as a separate repository for all purposes. diff --git a/README.rst b/README.rst deleted file mode 100644 index a1e142b77..000000000 --- a/README.rst +++ /dev/null @@ -1,19 +0,0 @@ -==== -FINN -==== - - -A Framework for Fast, Scalable Quantized Neural Network Inference - - -Description -=========== - -A longer description of your project goes here... - - -Note -==== - -This project has been set up using PyScaffold 3.2.1. For details and usage -information on PyScaffold see https://pyscaffold.org/. -- GitLab