{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:MGE2P3HESBXKBUGQFG6GQ6T3DL","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"8cdef3172bee098bd21434514700ababaa0b2e932f7eecf5a1b9f9b2d33fe25b","cross_cats_sorted":["cs.AI","cs.AR"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-08-09T19:35:10Z","title_canon_sha256":"eb1976fd835c921e5c55a804afdc991155b76dd5f9c03b06b9c6a4670528491d"},"schema_version":"1.0","source":{"id":"2408.05314","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2408.05314","created_at":"2026-07-05T08:54:37Z"},{"alias_kind":"arxiv_version","alias_value":"2408.05314v1","created_at":"2026-07-05T08:54:37Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2408.05314","created_at":"2026-07-05T08:54:37Z"},{"alias_kind":"pith_short_12","alias_value":"MGE2P3HESBXK","created_at":"2026-07-05T08:54:37Z"},{"alias_kind":"pith_short_16","alias_value":"MGE2P3HESBXKBUGQ","created_at":"2026-07-05T08:54:37Z"},{"alias_kind":"pith_short_8","alias_value":"MGE2P3HE","created_at":"2026-07-05T08:54:37Z"}],"graph_snapshots":[{"event_id":"sha256:bb7656e28c793fdf4f2af64a08cb4ceef0d3e87d8dc6184f77603b7dd2a74f8e","target":"graph","created_at":"2026-07-05T08:54:37Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2408.05314/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Implementing Machine Learning (ML) models on Field-Programmable Gate Arrays (FPGAs) is becoming increasingly popular across various domains as a low-latency and low-power solution that helps manage large data rates generated by continuously improving detectors. However, developing ML models for FPGAs is time-consuming, as optimization requires synthesis to evaluate FPGA area and latency, making the process slow and repetitive. This paper introduces a novel method to predict the resource utilization and inference latency of Neural Networks (NNs) before their synthesis and implementation on FPGA","authors_text":"Audrey C. Therrien, Hamza Ezzaoui Rahali, Mohammad Mehdi Rahimifar","cross_cats":["cs.AI","cs.AR"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-08-09T19:35:10Z","title":"rule4ml: An Open-Source Tool for Resource Utilization and Latency Estimation for ML Models on FPGA"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2408.05314","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:2b037c04285e3142863847cba4e0f9943e39df25d30daa92dc21de1c658bebf4","target":"record","created_at":"2026-07-05T08:54:37Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"8cdef3172bee098bd21434514700ababaa0b2e932f7eecf5a1b9f9b2d33fe25b","cross_cats_sorted":["cs.AI","cs.AR"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-08-09T19:35:10Z","title_canon_sha256":"eb1976fd835c921e5c55a804afdc991155b76dd5f9c03b06b9c6a4670528491d"},"schema_version":"1.0","source":{"id":"2408.05314","kind":"arxiv","version":1}},"canonical_sha256":"6189a7ece4906ea0d0d029bc687a7b1ae56ed3c990a2ebaec283855f14831e0e","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"6189a7ece4906ea0d0d029bc687a7b1ae56ed3c990a2ebaec283855f14831e0e","first_computed_at":"2026-07-05T08:54:37.111697Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T08:54:37.111697Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"ZXFcU7t7xwtt0sFwRappSu+RSU5nWjR9V/xnwKxJhx5lYOzpHqEdFPQDGs4MkNf7avZy3ghM5AQ+hHwSCQJrCA==","signature_status":"signed_v1","signed_at":"2026-07-05T08:54:37.112280Z","signed_message":"canonical_sha256_bytes"},"source_id":"2408.05314","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:2b037c04285e3142863847cba4e0f9943e39df25d30daa92dc21de1c658bebf4","sha256:bb7656e28c793fdf4f2af64a08cb4ceef0d3e87d8dc6184f77603b7dd2a74f8e"],"state_sha256":"524dd769e5f7423f0101857238111dac5d9ef96553c11563b80524478eb2db45"}