{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2020:WG5Z37DNYCWUUD2Z727J65WKTA","short_pith_number":"pith:WG5Z37DN","schema_version":"1.0","canonical_sha256":"b1bb9dfc6dc0ad4a0f59febe9f76ca98287018177fc733aec3030d923c6f4e6d","source":{"kind":"arxiv","id":"2004.03021","version":1},"attestation_state":"computed","paper":{"title":"LogicNets: Co-Designed Neural Networks and Circuits for Extreme-Throughput Applications","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AR","cs.LG"],"primary_cat":"eess.SP","authors_text":"Michaela Blott, Nicholas J. Fraser, Yaman Umuroglu, Yash Akhauri","submitted_at":"2020-04-06T22:15:41Z","abstract_excerpt":"Deployment of deep neural networks for applications that require very high throughput or extremely low latency is a severe computational challenge, further exacerbated by inefficiencies in mapping the computation to hardware. We present a novel method for designing neural network topologies that directly map to a highly efficient FPGA implementation. By exploiting the equivalence of artificial neurons with quantized inputs/outputs and truth tables, we can train quantized neural networks that can be directly converted to a netlist of truth tables, and subsequently deployed as a highly pipelinab"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2004.03021","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.SP","submitted_at":"2020-04-06T22:15:41Z","cross_cats_sorted":["cs.AR","cs.LG"],"title_canon_sha256":"ebe6abc9a3b82c759bcfc37b03c8e2a373287c1f3563f6c63fda846dd3634eeb","abstract_canon_sha256":"5c607d75abf93e0355203bf4d60307bafc47e9ecc354e6b39b23f638c83bb75e"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:53:22.488550Z","signature_b64":"zDzsHd0Sn1Nvx4vTPqpqEKlbrcK/yHAcCpHMoKgtp2mYXVYXQ6WtGOB/9fSUdFwFYZHG7ejN79E6EW2CZsCyAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b1bb9dfc6dc0ad4a0f59febe9f76ca98287018177fc733aec3030d923c6f4e6d","last_reissued_at":"2026-07-05T00:53:22.488093Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:53:22.488093Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"LogicNets: Co-Designed Neural Networks and Circuits for Extreme-Throughput Applications","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AR","cs.LG"],"primary_cat":"eess.SP","authors_text":"Michaela Blott, Nicholas J. Fraser, Yaman Umuroglu, Yash Akhauri","submitted_at":"2020-04-06T22:15:41Z","abstract_excerpt":"Deployment of deep neural networks for applications that require very high throughput or extremely low latency is a severe computational challenge, further exacerbated by inefficiencies in mapping the computation to hardware. We present a novel method for designing neural network topologies that directly map to a highly efficient FPGA implementation. By exploiting the equivalence of artificial neurons with quantized inputs/outputs and truth tables, we can train quantized neural networks that can be directly converted to a netlist of truth tables, and subsequently deployed as a highly pipelinab"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2004.03021","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2004.03021/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2004.03021","created_at":"2026-07-05T00:53:22.488150+00:00"},{"alias_kind":"arxiv_version","alias_value":"2004.03021v1","created_at":"2026-07-05T00:53:22.488150+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2004.03021","created_at":"2026-07-05T00:53:22.488150+00:00"},{"alias_kind":"pith_short_12","alias_value":"WG5Z37DNYCWU","created_at":"2026-07-05T00:53:22.488150+00:00"},{"alias_kind":"pith_short_16","alias_value":"WG5Z37DNYCWUUD2Z","created_at":"2026-07-05T00:53:22.488150+00:00"},{"alias_kind":"pith_short_8","alias_value":"WG5Z37DN","created_at":"2026-07-05T00:53:22.488150+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.29994","citing_title":"Precomputed 1D-CNNs for Atrial Fibrillation Detection on Tiny Smart Sensor Systems","ref_index":10,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/WG5Z37DNYCWUUD2Z727J65WKTA","json":"https://pith.science/pith/WG5Z37DNYCWUUD2Z727J65WKTA.json","graph_json":"https://pith.science/api/pith-number/WG5Z37DNYCWUUD2Z727J65WKTA/graph.json","events_json":"https://pith.science/api/pith-number/WG5Z37DNYCWUUD2Z727J65WKTA/events.json","paper":"https://pith.science/paper/WG5Z37DN"},"agent_actions":{"view_html":"https://pith.science/pith/WG5Z37DNYCWUUD2Z727J65WKTA","download_json":"https://pith.science/pith/WG5Z37DNYCWUUD2Z727J65WKTA.json","view_paper":"https://pith.science/paper/WG5Z37DN","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2004.03021&json=true","fetch_graph":"https://pith.science/api/pith-number/WG5Z37DNYCWUUD2Z727J65WKTA/graph.json","fetch_events":"https://pith.science/api/pith-number/WG5Z37DNYCWUUD2Z727J65WKTA/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/WG5Z37DNYCWUUD2Z727J65WKTA/action/timestamp_anchor","attest_storage":"https://pith.science/pith/WG5Z37DNYCWUUD2Z727J65WKTA/action/storage_attestation","attest_author":"https://pith.science/pith/WG5Z37DNYCWUUD2Z727J65WKTA/action/author_attestation","sign_citation":"https://pith.science/pith/WG5Z37DNYCWUUD2Z727J65WKTA/action/citation_signature","submit_replication":"https://pith.science/pith/WG5Z37DNYCWUUD2Z727J65WKTA/action/replication_record"}},"created_at":"2026-07-05T00:53:22.488150+00:00","updated_at":"2026-07-05T00:53:22.488150+00:00"}