{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:6YIPTG6KDH4NHKAYUKU2UUARVF","short_pith_number":"pith:6YIPTG6K","schema_version":"1.0","canonical_sha256":"f610f99bca19f8d3a818a2a9aa5011a948f32e7ccc9d91c419656ee734fce256","source":{"kind":"arxiv","id":"2312.07594","version":1},"attestation_state":"computed","paper":{"title":"On the Prediction of Hardware Security Properties of HLS Designs Using Graph Neural Networks","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CR","authors_text":"Aggelos Pikrakis, Amalia Artemis Koufopoulou, Athanasios Papadimitriou, David Hely, Mihalis Psarakis","submitted_at":"2023-12-11T10:13:53Z","abstract_excerpt":"High-level synthesis (HLS) tools have provided significant productivity enhancements to the design flow of digital systems in recent years, resulting in highly-optimized circuits, in terms of area and latency. Given the evolution of hardware attacks, which can render them vulnerable, it is essential to consider security as a significant aspect of the HLS design flow. Yet the need to evaluate a huge number of functionally equivalent de-signs of the HLS design space challenges hardware security evaluation methods (e.g., fault injection - FI campaigns). In this work, we propose an evaluation meth"},"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":"2312.07594","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CR","submitted_at":"2023-12-11T10:13:53Z","cross_cats_sorted":[],"title_canon_sha256":"fa59d20675ebf749923554533bc58c7f952fa5f99bf8709e86317bdd4c1fdc6e","abstract_canon_sha256":"dd1ddb5d1b570ce1344d3d17f8c3d76a33a8238385f79b8af1d30f0d31248921"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:23:33.229864Z","signature_b64":"XaUraRljwemWT54i6ccCsM2gaWeMvd8pftNT8H2t4eRhdZcwfXdFmiO55D9DN7tnIi/1a5JRz3tWMMq3KK/0BQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f610f99bca19f8d3a818a2a9aa5011a948f32e7ccc9d91c419656ee734fce256","last_reissued_at":"2026-07-05T07:23:33.229434Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:23:33.229434Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"On the Prediction of Hardware Security Properties of HLS Designs Using Graph Neural Networks","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CR","authors_text":"Aggelos Pikrakis, Amalia Artemis Koufopoulou, Athanasios Papadimitriou, David Hely, Mihalis Psarakis","submitted_at":"2023-12-11T10:13:53Z","abstract_excerpt":"High-level synthesis (HLS) tools have provided significant productivity enhancements to the design flow of digital systems in recent years, resulting in highly-optimized circuits, in terms of area and latency. Given the evolution of hardware attacks, which can render them vulnerable, it is essential to consider security as a significant aspect of the HLS design flow. Yet the need to evaluate a huge number of functionally equivalent de-signs of the HLS design space challenges hardware security evaluation methods (e.g., fault injection - FI campaigns). In this work, we propose an evaluation meth"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2312.07594","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/2312.07594/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":"2312.07594","created_at":"2026-07-05T07:23:33.229493+00:00"},{"alias_kind":"arxiv_version","alias_value":"2312.07594v1","created_at":"2026-07-05T07:23:33.229493+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2312.07594","created_at":"2026-07-05T07:23:33.229493+00:00"},{"alias_kind":"pith_short_12","alias_value":"6YIPTG6KDH4N","created_at":"2026-07-05T07:23:33.229493+00:00"},{"alias_kind":"pith_short_16","alias_value":"6YIPTG6KDH4NHKAY","created_at":"2026-07-05T07:23:33.229493+00:00"},{"alias_kind":"pith_short_8","alias_value":"6YIPTG6K","created_at":"2026-07-05T07:23:33.229493+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/6YIPTG6KDH4NHKAYUKU2UUARVF","json":"https://pith.science/pith/6YIPTG6KDH4NHKAYUKU2UUARVF.json","graph_json":"https://pith.science/api/pith-number/6YIPTG6KDH4NHKAYUKU2UUARVF/graph.json","events_json":"https://pith.science/api/pith-number/6YIPTG6KDH4NHKAYUKU2UUARVF/events.json","paper":"https://pith.science/paper/6YIPTG6K"},"agent_actions":{"view_html":"https://pith.science/pith/6YIPTG6KDH4NHKAYUKU2UUARVF","download_json":"https://pith.science/pith/6YIPTG6KDH4NHKAYUKU2UUARVF.json","view_paper":"https://pith.science/paper/6YIPTG6K","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2312.07594&json=true","fetch_graph":"https://pith.science/api/pith-number/6YIPTG6KDH4NHKAYUKU2UUARVF/graph.json","fetch_events":"https://pith.science/api/pith-number/6YIPTG6KDH4NHKAYUKU2UUARVF/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/6YIPTG6KDH4NHKAYUKU2UUARVF/action/timestamp_anchor","attest_storage":"https://pith.science/pith/6YIPTG6KDH4NHKAYUKU2UUARVF/action/storage_attestation","attest_author":"https://pith.science/pith/6YIPTG6KDH4NHKAYUKU2UUARVF/action/author_attestation","sign_citation":"https://pith.science/pith/6YIPTG6KDH4NHKAYUKU2UUARVF/action/citation_signature","submit_replication":"https://pith.science/pith/6YIPTG6KDH4NHKAYUKU2UUARVF/action/replication_record"}},"created_at":"2026-07-05T07:23:33.229493+00:00","updated_at":"2026-07-05T07:23:33.229493+00:00"}