{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2022:SINRAYDQXKSF5HIUYM2MVHJT34","short_pith_number":"pith:SINRAYDQ","canonical_record":{"source":{"id":"2206.13734","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AR","submitted_at":"2022-06-28T03:37:31Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"bb5a56ff78706b9f7bc4c0e0e73c9c86b50ab326f74acb9c5c0bc9b7d403374f","abstract_canon_sha256":"12fc95459257decfd1763368610d99fdefb12758981517de571b1b20e399da9c"},"schema_version":"1.0"},"canonical_sha256":"921b106070baa45e9d14c334ca9d33df231337de4aba8695e4eb8d879cb77c70","source":{"kind":"arxiv","id":"2206.13734","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2206.13734","created_at":"2026-07-05T04:35:24Z"},{"alias_kind":"arxiv_version","alias_value":"2206.13734v1","created_at":"2026-07-05T04:35:24Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2206.13734","created_at":"2026-07-05T04:35:24Z"},{"alias_kind":"pith_short_12","alias_value":"SINRAYDQXKSF","created_at":"2026-07-05T04:35:24Z"},{"alias_kind":"pith_short_16","alias_value":"SINRAYDQXKSF5HIU","created_at":"2026-07-05T04:35:24Z"},{"alias_kind":"pith_short_8","alias_value":"SINRAYDQ","created_at":"2026-07-05T04:35:24Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2022:SINRAYDQXKSF5HIUYM2MVHJT34","target":"record","payload":{"canonical_record":{"source":{"id":"2206.13734","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AR","submitted_at":"2022-06-28T03:37:31Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"bb5a56ff78706b9f7bc4c0e0e73c9c86b50ab326f74acb9c5c0bc9b7d403374f","abstract_canon_sha256":"12fc95459257decfd1763368610d99fdefb12758981517de571b1b20e399da9c"},"schema_version":"1.0"},"canonical_sha256":"921b106070baa45e9d14c334ca9d33df231337de4aba8695e4eb8d879cb77c70","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:35:24.342384Z","signature_b64":"EeJvBz+PEwPfqKXAWphEbbayNkMsuZmuywIpDdmv2pIFWxYalNEDE5/sd7PKZLX3hM3r8i8TI/XAbdjkqKU3Ag==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"921b106070baa45e9d14c334ca9d33df231337de4aba8695e4eb8d879cb77c70","last_reissued_at":"2026-07-05T04:35:24.341845Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:35:24.341845Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2206.13734","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T04:35:24Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"QD8KJI5GIm12N+HldDl5hVl940PxHgRNgLWECmkc6xFfoKd8aw7BZu0XcjVKHAl8GmcEh4gjb6RNMn+oCyAuDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-18T18:01:34.859770Z"},"content_sha256":"274182119249120f4398b0fd923a0c9eb7998e9f862256661a81523a321585da","schema_version":"1.0","event_id":"sha256:274182119249120f4398b0fd923a0c9eb7998e9f862256661a81523a321585da"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2022:SINRAYDQXKSF5HIUYM2MVHJT34","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"H-GCN: A Graph Convolutional Network Accelerator on Versal ACAP Architecture","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.AR","authors_text":"Ang Li, Anqi Guo, Chengming Zhang, Dingwen Tao, Jiannan Tian, Martin Herbordt, Tong Geng","submitted_at":"2022-06-28T03:37:31Z","abstract_excerpt":"Graph Neural Networks (GNNs) have drawn tremendous attention due to their unique capability to extend Machine Learning (ML) approaches to applications broadly-defined as having unstructured data, especially graphs. Compared with other Machine Learning (ML) modalities, the acceleration of Graph Neural Networks (GNNs) is more challenging due to the irregularity and heterogeneity derived from graph typologies. Existing efforts, however, have focused mainly on handling graphs' irregularity and have not studied their heterogeneity.\n  To this end we propose H-GCN, a PL (Programmable Logic) and AIE ("},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2206.13734","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/2206.13734/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T04:35:24Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"hG+Fn321D88E9NxYTQGRVoH8PNpTUYSdqhUG+gwzyx/CEyQdNn8NTvTFD2sK0/geFh1XdzC8kmMVlg/8AWL3BA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-18T18:01:34.860435Z"},"content_sha256":"428611795d0ef4c648db8934d774717f5e99894fe916a33d5e913a22f138649c","schema_version":"1.0","event_id":"sha256:428611795d0ef4c648db8934d774717f5e99894fe916a33d5e913a22f138649c"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/SINRAYDQXKSF5HIUYM2MVHJT34/bundle.json","state_url":"https://pith.science/pith/SINRAYDQXKSF5HIUYM2MVHJT34/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/SINRAYDQXKSF5HIUYM2MVHJT34/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-18T18:01:34Z","links":{"resolver":"https://pith.science/pith/SINRAYDQXKSF5HIUYM2MVHJT34","bundle":"https://pith.science/pith/SINRAYDQXKSF5HIUYM2MVHJT34/bundle.json","state":"https://pith.science/pith/SINRAYDQXKSF5HIUYM2MVHJT34/state.json","well_known_bundle":"https://pith.science/.well-known/pith/SINRAYDQXKSF5HIUYM2MVHJT34/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:SINRAYDQXKSF5HIUYM2MVHJT34","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":"12fc95459257decfd1763368610d99fdefb12758981517de571b1b20e399da9c","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AR","submitted_at":"2022-06-28T03:37:31Z","title_canon_sha256":"bb5a56ff78706b9f7bc4c0e0e73c9c86b50ab326f74acb9c5c0bc9b7d403374f"},"schema_version":"1.0","source":{"id":"2206.13734","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2206.13734","created_at":"2026-07-05T04:35:24Z"},{"alias_kind":"arxiv_version","alias_value":"2206.13734v1","created_at":"2026-07-05T04:35:24Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2206.13734","created_at":"2026-07-05T04:35:24Z"},{"alias_kind":"pith_short_12","alias_value":"SINRAYDQXKSF","created_at":"2026-07-05T04:35:24Z"},{"alias_kind":"pith_short_16","alias_value":"SINRAYDQXKSF5HIU","created_at":"2026-07-05T04:35:24Z"},{"alias_kind":"pith_short_8","alias_value":"SINRAYDQ","created_at":"2026-07-05T04:35:24Z"}],"graph_snapshots":[{"event_id":"sha256:428611795d0ef4c648db8934d774717f5e99894fe916a33d5e913a22f138649c","target":"graph","created_at":"2026-07-05T04:35:24Z","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/2206.13734/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Graph Neural Networks (GNNs) have drawn tremendous attention due to their unique capability to extend Machine Learning (ML) approaches to applications broadly-defined as having unstructured data, especially graphs. Compared with other Machine Learning (ML) modalities, the acceleration of Graph Neural Networks (GNNs) is more challenging due to the irregularity and heterogeneity derived from graph typologies. Existing efforts, however, have focused mainly on handling graphs' irregularity and have not studied their heterogeneity.\n  To this end we propose H-GCN, a PL (Programmable Logic) and AIE (","authors_text":"Ang Li, Anqi Guo, Chengming Zhang, Dingwen Tao, Jiannan Tian, Martin Herbordt, Tong Geng","cross_cats":["cs.LG"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AR","submitted_at":"2022-06-28T03:37:31Z","title":"H-GCN: A Graph Convolutional Network Accelerator on Versal ACAP Architecture"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2206.13734","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":"sha256:274182119249120f4398b0fd923a0c9eb7998e9f862256661a81523a321585da","target":"record","created_at":"2026-07-05T04:35:24Z","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":"12fc95459257decfd1763368610d99fdefb12758981517de571b1b20e399da9c","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AR","submitted_at":"2022-06-28T03:37:31Z","title_canon_sha256":"bb5a56ff78706b9f7bc4c0e0e73c9c86b50ab326f74acb9c5c0bc9b7d403374f"},"schema_version":"1.0","source":{"id":"2206.13734","kind":"arxiv","version":1}},"canonical_sha256":"921b106070baa45e9d14c334ca9d33df231337de4aba8695e4eb8d879cb77c70","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"921b106070baa45e9d14c334ca9d33df231337de4aba8695e4eb8d879cb77c70","first_computed_at":"2026-07-05T04:35:24.341845Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T04:35:24.341845Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"EeJvBz+PEwPfqKXAWphEbbayNkMsuZmuywIpDdmv2pIFWxYalNEDE5/sd7PKZLX3hM3r8i8TI/XAbdjkqKU3Ag==","signature_status":"signed_v1","signed_at":"2026-07-05T04:35:24.342384Z","signed_message":"canonical_sha256_bytes"},"source_id":"2206.13734","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:274182119249120f4398b0fd923a0c9eb7998e9f862256661a81523a321585da","sha256:428611795d0ef4c648db8934d774717f5e99894fe916a33d5e913a22f138649c"],"state_sha256":"a14dcf8139a491549f2d9250fa4a20c7c954f37a88ae494f3a64f67fe75c3e37"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"dEfl2ItGKPYxqkoCp5u35tevVVQm277i2wLkrZ4NvXOtfX7YyC9GLHz55GRy98VpLV4yHucb0uGg7XF8h5P8Aw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-18T18:01:34.864760Z","bundle_sha256":"ff7e1a86ffc33c246a1f3ba1e0a648553375074a53c1c4740e401b9bc9fc564e"}}