{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/fast-inference-of-deep-neural-networks-in","title":"Fast inference of deep neural networks in FPGAs for particle physics","arxiv_id":"1804.06913","date":"2018-04-16","proceeding":null,"authors":["Javier Duarte","Song Han","Philip Harris","Sergo Jindariani","Edward Kreinar","Benjamin Kreis","Jennifer Ngadiuba","Maurizio Pierini","Ryan Rivera","Nhan Tran","Zhenbin Wu"],"abstract":"Recent results at the Large Hadron Collider (LHC) have pointed to enhanced\nphysics capabilities through the improvement of the real-time event processing\ntechniques. Machine learning methods are ubiquitous and have proven to be very\npowerful in LHC physics, and particle physics as a whole. However, exploration\nof the use of such techniques in low-latency, low-power FPGA hardware has only\njust begun. FPGA-based trigger and data acquisition (DAQ) systems have\nextremely low, sub-microsecond latency requirements that are unique to particle\nphysics. We present a case study for neural network inference in FPGAs focusing\non a classifier for jet substructure which would enable, among many other\nphysics scenarios, searches for new dark sector particles and novel\nmeasurements of the Higgs boson. While we focus on a specific example, the\nlessons are far-reaching. We develop a package based on High-Level Synthesis\n(HLS) called hls4ml to build machine learning models in FPGAs. The use of HLS\nincreases accessibility across a broad user community and allows for a drastic\ndecrease in firmware development time. We map out FPGA resource usage and\nlatency versus neural network hyperparameters to identify the problems in\nparticle physics that would benefit from performing neural network inference\nwith FPGAs. For our example jet substructure model, we fit well within the\navailable resources of modern FPGAs with a latency on the scale of 100 ns.","url_abs":"http://arxiv.org/abs/1804.06913v3","url_pdf":"http://arxiv.org/pdf/1804.06913v3.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"fast-inference-of-deep-neural-networks-in","repo_url":"https://github.com/hls-fpga-machine-learning/hls4ml","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"tf","reach":{"status":"ok","spdx":"Apache-2.0"}},{"paper_slug":"fast-inference-of-deep-neural-networks-in","repo_url":"https://github.com/TheKivs/LHC_Jet_Tagging","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}}],"tasks":[{"task_slug":"machine-learning","task_name":"BIG-bench Machine Learning"},{"task_slug":"high-level-synthesis","task_name":"High-Level Synthesis"}],"methods":[],"datasets_introduced":[{"slug":"hls4ml-lhc-jet-dataset","name":"hls4ml LHC Jet dataset","full_name":"hls4ml LHC Jet dataset (100 particles)"}],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1804.06913","atlas_url":"https://app.syntology.ai/?focus=1804.06913","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}