Fermilab: Bridging Machine Learning and Hardware Design with hls4ml and Catapult HLS

hls4ml is an open-source framework that transforms machine learning models from popular software libraries such as TensorFlow and PyTorch into hardware accelerators for FPGA and ASIC flows through High-Level Synthesis (HLS). It aims to optimize ML models for the constraints of hardware implementation while preserving accuracy. This optimization is achieved through a carefully tailored library of ML layers for HLS, a simplified set of configuration knobs, and techniques, such as quantization-aware training. The hls4ml framework facilitates a streamlined HLS design flow, allowing early estimates and accelerating the power, performance, and area optimization. Originally developed to meet the needs of particle physics experiments, hls4ml has broadened its application scope to support various scientific and industrial projects where low latency, high throughput, and energy efficiency are crucial. In this presentation, we introduce our partnership with Siemens to provide full integration with the Catapult HLS design and verification flow, describe some projects that have already benefitted from hls4ml, and outline our future directions.