27 Jan 2021 - Yaman Umuroglu
This event has now concluded. You can find the materials at the bottom of this page.
We’re delighted to announce a two-hour FINN tutorial as part of the FPGA’21 conference. Details are as follows:
Mixing machine learning into high-throughput, low-latency edge applications needs co-designed solutions to meet the performance requirements. Quantized Neural Networks (QNNs) combined with custom FPGA dataflow implementations offer a good balance of performance and flexibility, but building such implementations by hand is difficult and time-consuming.
In this tutorial, we will introduce FINN, an open-source experimental framework by Xilinx Research Labs to help the broader community explore QNN inference on FPGAs. Providing a full-stack solution from quantization-aware training to bitfile, FINN generates high-performance dataflow-style FPGA architectures customized for each network. Participants will be introduced to efficient inference with QNNs and streaming dataflow architectures, the components of the project’s open-source ecosystem, and gain hands-on experience training a quantized neural network with Brevitas and deploying it with FINN.
Some prior knowledge of FPGAs, Vivado HLS, PyTorch and neural network training is recommended, but not required.
This will be a virtual event, with a Zoom video call and a hands-on Jupyter notebook lab. Registered participants will get access to a FINN setup running in the cloud. There are no special technical requirements besides a browser and Zoom client.