{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:KYNO2VCT2PWPT65I6MA7I64QGD","short_pith_number":"pith:KYNO2VCT","schema_version":"1.0","canonical_sha256":"561aed5453d3ecf9fba8f301f47b9030ec8ace39686ede62de3681f23b73d7dc","source":{"kind":"arxiv","id":"2509.06289","version":1},"attestation_state":"computed","paper":{"title":"A Spatio-Temporal Graph Neural Networks Approach for Predicting Silent Data Corruption inducing Circuit-Level Faults","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":["cs.AR","cs.ET"],"primary_cat":"cs.LG","authors_text":"Hiroshi Kai, Hiroshi Takahashi, Ruijun Ma, Senling Wang, Shaoqi Wei, Tianming Ni, Xiaoqing Wen, Yoshinobu Higami","submitted_at":"2025-09-08T02:23:51Z","abstract_excerpt":"Silent Data Errors (SDEs) from time-zero defects and aging degrade safety-critical systems. Functional testing detects SDE-related faults but is expensive to simulate. We present a unified spatio-temporal graph convolutional network (ST-GCN) for fast, accurate prediction of long-cycle fault impact probabilities (FIPs) in large sequential circuits, supporting quantitative risk assessment. Gate-level netlists are modeled as spatio-temporal graphs to capture topology and signal timing; dedicated spatial and temporal encoders predict multi-cycle FIPs efficiently. On ISCAS-89 benchmarks, the method"},"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":"2509.06289","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2025-09-08T02:23:51Z","cross_cats_sorted":["cs.AR","cs.ET"],"title_canon_sha256":"842fc33197818cbdac583a3aa2277df746a4ef1bb876209fa23df7b2acc9441c","abstract_canon_sha256":"fb99d55060fae2826f9f5373661f5de10281e290ae4fec23d95ad60a90f67cdc"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T12:06:33.130076Z","signature_b64":"Jt++QtSdqhkfRCGjcJn+a37A1CX861R5i7qGXYFSJZeFWtGcZd8agUMtTOP53q85ImS21BRu0O0d5oRhOheVCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"561aed5453d3ecf9fba8f301f47b9030ec8ace39686ede62de3681f23b73d7dc","last_reissued_at":"2026-07-05T12:06:33.129521Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T12:06:33.129521Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Spatio-Temporal Graph Neural Networks Approach for Predicting Silent Data Corruption inducing Circuit-Level Faults","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":["cs.AR","cs.ET"],"primary_cat":"cs.LG","authors_text":"Hiroshi Kai, Hiroshi Takahashi, Ruijun Ma, Senling Wang, Shaoqi Wei, Tianming Ni, Xiaoqing Wen, Yoshinobu Higami","submitted_at":"2025-09-08T02:23:51Z","abstract_excerpt":"Silent Data Errors (SDEs) from time-zero defects and aging degrade safety-critical systems. Functional testing detects SDE-related faults but is expensive to simulate. We present a unified spatio-temporal graph convolutional network (ST-GCN) for fast, accurate prediction of long-cycle fault impact probabilities (FIPs) in large sequential circuits, supporting quantitative risk assessment. Gate-level netlists are modeled as spatio-temporal graphs to capture topology and signal timing; dedicated spatial and temporal encoders predict multi-cycle FIPs efficiently. On ISCAS-89 benchmarks, the method"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2509.06289","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/2509.06289/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":"2509.06289","created_at":"2026-07-05T12:06:33.129579+00:00"},{"alias_kind":"arxiv_version","alias_value":"2509.06289v1","created_at":"2026-07-05T12:06:33.129579+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2509.06289","created_at":"2026-07-05T12:06:33.129579+00:00"},{"alias_kind":"pith_short_12","alias_value":"KYNO2VCT2PWP","created_at":"2026-07-05T12:06:33.129579+00:00"},{"alias_kind":"pith_short_16","alias_value":"KYNO2VCT2PWPT65I","created_at":"2026-07-05T12:06:33.129579+00:00"},{"alias_kind":"pith_short_8","alias_value":"KYNO2VCT","created_at":"2026-07-05T12:06:33.129579+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/KYNO2VCT2PWPT65I6MA7I64QGD","json":"https://pith.science/pith/KYNO2VCT2PWPT65I6MA7I64QGD.json","graph_json":"https://pith.science/api/pith-number/KYNO2VCT2PWPT65I6MA7I64QGD/graph.json","events_json":"https://pith.science/api/pith-number/KYNO2VCT2PWPT65I6MA7I64QGD/events.json","paper":"https://pith.science/paper/KYNO2VCT"},"agent_actions":{"view_html":"https://pith.science/pith/KYNO2VCT2PWPT65I6MA7I64QGD","download_json":"https://pith.science/pith/KYNO2VCT2PWPT65I6MA7I64QGD.json","view_paper":"https://pith.science/paper/KYNO2VCT","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2509.06289&json=true","fetch_graph":"https://pith.science/api/pith-number/KYNO2VCT2PWPT65I6MA7I64QGD/graph.json","fetch_events":"https://pith.science/api/pith-number/KYNO2VCT2PWPT65I6MA7I64QGD/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/KYNO2VCT2PWPT65I6MA7I64QGD/action/timestamp_anchor","attest_storage":"https://pith.science/pith/KYNO2VCT2PWPT65I6MA7I64QGD/action/storage_attestation","attest_author":"https://pith.science/pith/KYNO2VCT2PWPT65I6MA7I64QGD/action/author_attestation","sign_citation":"https://pith.science/pith/KYNO2VCT2PWPT65I6MA7I64QGD/action/citation_signature","submit_replication":"https://pith.science/pith/KYNO2VCT2PWPT65I6MA7I64QGD/action/replication_record"}},"created_at":"2026-07-05T12:06:33.129579+00:00","updated_at":"2026-07-05T12:06:33.129579+00:00"}