{"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/sets-are-all-you-need-ultrafast-jet","title":"Ultrafast jet classification on FPGAs for the HL-LHC","arxiv_id":"2402.01876","date":"2024-02-02","proceeding":null,"authors":["Patrick Odagiu","Zhiqiang Que","Javier Duarte","Johannes Haller","Gregor Kasieczka","Artur Lobanov","Vladimir Loncar","Wayne Luk","Jennifer Ngadiuba","Maurizio Pierini","Philipp Rincke","Arpita Seksaria","Sioni Summers","Andre Sznajder","Alexander Tapper","Thea K. Aarrestad"],"abstract":"Three machine learning models are used to perform jet origin classification. These models are optimized for deployment on a field-programmable gate array device. In this context, we demonstrate how latency and resource consumption scale with the input size and choice of algorithm. Moreover, the models proposed here are designed to work on the type of data and under the foreseen conditions at the CERN LHC during its high-luminosity phase. Through quantization-aware training and efficient synthetization for a specific field programmable gate array, we show that $O(100)$ ns inference of complex architectures such as Deep Sets and Interaction Networks is feasible at a relatively low computational resource cost.","url_abs":"https://arxiv.org/abs/2402.01876v2","url_pdf":"https://arxiv.org/pdf/2402.01876v2.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":"sets-are-all-you-need-ultrafast-jet","repo_url":"https://github.com/fastmachinelearning/l1-jet-id","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"Apache-2.0"}}],"tasks":[{"task_slug":"quantization","task_name":"Quantization"}],"methods":[{"method_slug":"deep-sets","method_name":"Deep Sets"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/2402.01876","atlas_url":"https://app.syntology.ai/?focus=2402.01876","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.01876"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/fastmachinelearning/l1-jet-id","reach":{"status":"ok","spdx":"Apache-2.0"}}],"summary":{"ran":5,"unverified":2},"by_repo_kind":{"official":{"samples":7,"ran":5,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":0,"samples":[{"code_sha256_prefix":"d2b7a88d5b9b212e","entry":"calculate_accuracy","repo":"fastmachinelearning/l1-jet-id","repo_kind":"official","path":"fast_jetclass/intnet/synthesize.py","file_url":"https://github.com/fastmachinelearning/l1-jet-id/blob/HEAD/fast_jetclass/intnet/synthesize.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"d2b7a88d5b9b212e"}},{"code_sha256_prefix":"cd057a57caf451b6","entry":"choose_aggregator","repo":"fastmachinelearning/l1-jet-id","repo_kind":"official","path":"fast_jetclass/intnet/intnet_synth.py","file_url":"https://github.com/fastmachinelearning/l1-jet-id/blob/HEAD/fast_jetclass/intnet/intnet_synth.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"cd057a57caf451b6"}},{"code_sha256_prefix":"8517c07d9e41fb50","entry":"format_qactivation","repo":"fastmachinelearning/l1-jet-id","repo_kind":"official","path":"fast_jetclass/intnet/intnet_quantised.py","file_url":"https://github.com/fastmachinelearning/l1-jet-id/blob/HEAD/fast_jetclass/intnet/intnet_quantised.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"8517c07d9e41fb50"}},{"code_sha256_prefix":"456379e806b7c263","entry":"format_quantiser","repo":"fastmachinelearning/l1-jet-id","repo_kind":"official","path":"fast_jetclass/intnet/intnet_quantised.py","file_url":"https://github.com/fastmachinelearning/l1-jet-id/blob/HEAD/fast_jetclass/intnet/intnet_quantised.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"456379e806b7c263"}},{"code_sha256_prefix":"bcbcecbe4a8a8046","entry":"parse_node_edge_projection_layer","repo":"fastmachinelearning/l1-jet-id","repo_kind":"official","path":"fast_jetclass/intnet/hls_node_edge_projection.py","file_url":"https://github.com/fastmachinelearning/l1-jet-id/blob/HEAD/fast_jetclass/intnet/hls_node_edge_projection.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"bcbcecbe4a8a8046"}},{"code_sha256_prefix":"2f8b2a4ebd7103c3","entry":"load_optimizer","repo":"fastmachinelearning/l1-jet-id","repo_kind":"official","path":"fast_jetclass/intnet/util.py","file_url":"https://github.com/fastmachinelearning/l1-jet-id/blob/HEAD/fast_jetclass/intnet/util.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"2f8b2a4ebd7103c3"}},{"code_sha256_prefix":"0bab166d3ff8db47","entry":"parse_bcast_subtract_layer","repo":"fastmachinelearning/l1-jet-id","repo_kind":"official","path":"fast_jetclass/deepsets/synth_equivariant.py","file_url":"https://github.com/fastmachinelearning/l1-jet-id/blob/HEAD/fast_jetclass/deepsets/synth_equivariant.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"0bab166d3ff8db47"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}