hls4ml: A Flexible, Open-Source Platform for Deep Learning Acceleration on Reconfigurable Hardware
Year: 2025
Type: article
Abstract: 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 paper, 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.
Authors: Schulte, Jan-Frederik, Ramhorst, Benjamin, Sun, Chang, Mitrevski, Jovan, Ghielmetti, Nicolò, Lupi, Enrico, Danopoulos, Dimitrios, Loncar, Vladimir, Duarte, Javier, Burnette, David, Laatu, Lauri, Tzelepis, Stylianos, Axiotis, Konstantinos, Berthet, Quentin, Wang, Haoyan, White, Paul, Demirsoy, Suleyman, Colombo, Marco, Aarrestad, Thea, Summers, Sioni, Pierini, Maurizio, Di Guglielmo, Giuseppe, Ngadiuba, Jennifer, Campos, Javier, Hawks, Ben, Gandrakota, Abhijith, Fahim, Farah, Tran, Nhan, Constantinides, George, Que, Zhiqiang, Luk, Wayne, Tapper, Alexander, Hoang, Duc, Paladino, Noah, Harris, Philip, Lai, Bo-Cheng, Valentin, Manuel, Forelli, Ryan, Ogrenci, Seda, Gerlach, Lino, Flynn, Rian, Liu, Mia, Diaz, Daniel, Khoda, Elham, Quinnan, Melissa, Solares, Russell, Parajuli, Santosh, Neubauer, Mark, Herwig, Christian, Tsoi, Ho Fung, Rankin, Dylan, Hsu, Shih-Chieh, Hauck, Scott
Institutions: ETH Zurich, California Institute of Technology, European Organization for Nuclear Research, Fermi National Accelerator Laboratory, Purdue University West Lafayette, Institute of Physics Belgrade, Imperial College London, University of Geneva, National Technical University of Athens, Altera (United States), National Yang Ming Chiao Tung University, Massachusetts Institute of Technology, Northwestern University, Princeton University, University of California San Diego, University of Michigan, University of Pennsylvania, University of Washington
Language: English
Subfield: Hardware and Architecture
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Funders: National Science Foundation, U.S. Department of Energy, Alfred P. Sloan Foundation, Research Corporation for Science Advancement, Northwestern University, CERN, UK Research and Innovation, Imperial College London, European Commission, Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung, Eidgenössische Technische Hochschule Zürich, China Scholarship Council, Office of Science, International Institute for Nanotechnology, Northwestern University, Engineering and Physical Sciences Research Council, Science and Technology Facilities Council, High Energy Physics
Awards: RESEARCH IN HIGH ENERGY PHYSICS AT THE UNIVERSITY OF ILLINOIS ON THE ATLAS EXPERIMENT, G1387744893, SEARCHING FOR NEW PHYSICS WITH REAL-TIME ANOMALY DETECTION, G1959365839, G2091642509, G2926102538, POSE: Phase II: An Open Source Ecosystem for Collaborative Rapid Design of Edge AI Hardware Accelerators for Integrated Data Analysis and Discovery, Centre for Spatial Computational Learning, G434337789, SONNETS: Scalability Oriented Novel Network of Event Triggered Systems, DART: Design Accelerators by Regulating Transformations, Event-based parallel computing - partially ordered event-triggered systems (POETS), G5574724152, Unleashing AI potential to foster space accessibility and novel Earth Observation services creation., Physique des plasmas et des tokamaks, utilisation d'un diagnostic avancé (tomographie des rayons X-mous)., G6471802762, HDR Institute: Accelerated AI Algorithms for Data-Driven Discovery, RESEARCH ACCOMPLISHMENTS AND GOALS OF THE EXPERIMENTAL HIGH ENERGY PHYSICS GROUP, RESEARCH ACCOMPLISHMENTS AND GOALS OF THE EXPERIMENTAL HIGH ENERGY PHYSICS GROUP, REAL-TIME ARTIFICIAL INTELLIGENCE FOR PARTICLE RECONSTRUCTION AND HIGGS PHYSICS, REAL-TIME ARTIFICIAL INTELLIGENCE FOR PARTICLE RECONSTRUCTION AND HIGGS PHYSICS, Detecting New Physics at 40 Megahertz: Scouting for anomalous events with unsupervised AI in the CMS hardware trigger, G8890396195, The study of elementary particles and their interactions (Consolidated Grant 2022 - 2025), G8989177975
OpenAlex ID: W4416964958
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