hls4ml: A Flexible, Open Source Platform for Deep Learning Acceleration on Reconfigurable Hardware
J. F. Schulte, Benjamin Ramhorst, Chang Sun, Jovan Mitrevski, Nicolò Ghielmetti, Enrico Lupi, Dimitrios Danopoulos, Vladimir Lončar, Javier Mauricio Duarte, David Burnette, L. A. O. Laatu, Stylianos Tzelepis, K. Axiotis, Quentin Berthet, Haoyan Wang, Paul White, S.S. Demirsoy, Marco Colombo, T. K. Aarrestad, S. Summers · ACM Transactions on Reconfigurable Technology and Systems · 2026
We present hls4ml , a free and open source platform that translates machine learning (ML) models from modern deep learning frameworks into high-level synthesis (HLS) code that can be integrated into full designs for field-programmable gate arrays (FPGAs) or application-specific integrated circuits (ASICs). With its flexible and modular design, hls4ml supports a large number of deep learning frameworks and can target HLS compilers from several vendors, including Vitis HLS, Intel oneAPI and Catapult HLS. Together with a wider eco-system for software-hardware co-design, hls4ml has enabled the acceleration of ML inference in a wide range of commercial and scientific applications where low latency, resource usage, and power consumption are critical. In this article, we describe the structure and functionality of the hls4ml platform. The overarching design considerations for the generated HLS code are discussed, together with selected performance results.