Speaker
Description
FPGAs provide unique advantages in the realm of machine learning acceleration. Unlike CPUs and GPUs, FPGAs allow for custom parallelism, data type precision and dataflow tailored specifically to the workload. Their reconfigurability enables the design of optimised hardware circuits that can reduce latency, power consumption, and improve throughput. Some common examples of FPGA-accelerated neural networks include particle classification, in-network traffic sniffing, and image segmentation for autonomous vehicles.
In this tutorial, we will introduce and hold a hands-on demo on hls4ml, an open-source library for real-time deployment of neural networks on FPGAs. hls4ml allows a seamless conversion from high-level models (e.g., from Keras or PyTorch) to low-latency, low-power FPGA designs. In this tutorial, we will cover the design choices behind hls4ml, from deeply pipelined dataflow architectures to model quantization and pruning. The hands-on demo will allow participants to experiment with hls4ml’s Python API and try out the following concepts:
- Quantization-aware training with QKeras
- Model conversion with hls4ml
- Analysis of model latency and resource utilisation
- Tuning of model resources and latency
Finally, the tutorial will conclude with a live demo of the model inference on a real FPGA.