{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:UFQH6XVD2UGCTF2OK6QV4GCAVW","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":"5192315f79fef0ddbc1c6f2e38e8db4fa34fee6f4e74e00ebb9716d0ef0078d7","cross_cats_sorted":["eess.SP"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AR","submitted_at":"2023-11-16T10:58:34Z","title_canon_sha256":"cd02a3156b311170b5c1faaa39444ae1b4742fc095e624951f775a310a4cb226"},"schema_version":"1.0","source":{"id":"2311.09775","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2311.09775","created_at":"2026-07-05T07:13:32Z"},{"alias_kind":"arxiv_version","alias_value":"2311.09775v1","created_at":"2026-07-05T07:13:32Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2311.09775","created_at":"2026-07-05T07:13:32Z"},{"alias_kind":"pith_short_12","alias_value":"UFQH6XVD2UGC","created_at":"2026-07-05T07:13:32Z"},{"alias_kind":"pith_short_16","alias_value":"UFQH6XVD2UGCTF2O","created_at":"2026-07-05T07:13:32Z"},{"alias_kind":"pith_short_8","alias_value":"UFQH6XVD","created_at":"2026-07-05T07:13:32Z"}],"graph_snapshots":[{"event_id":"sha256:23e3bae2f169b7b6321482a0b06c447f51795e945941b87f8133f3e398f77c23","target":"graph","created_at":"2026-07-05T07:13:32Z","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/2311.09775/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Graph Neural Networks (GNNs) are becoming a promising technique in various domains due to their excellent capabilities in modeling non-Euclidean data. Although a spectrum of accelerators has been proposed to accelerate the inference of GNNs, our analysis demonstrates that the latency and energy consumption induced by DRAM access still significantly impedes the improvement of performance and energy efficiency. To address this issue, we propose a Memory-Efficient GNN Accelerator (MEGA) through algorithm and hardware co-design in this work. Specifically, at the algorithm level, through an in-dept","authors_text":"Fanrong Li, Gang Li, Jian Cheng, Qinghao Hu, Xiaoyao Liang, Zejian Liu, Zeyu Zhu, Zitao Mo","cross_cats":["eess.SP"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AR","submitted_at":"2023-11-16T10:58:34Z","title":"MEGA: A Memory-Efficient GNN Accelerator Exploiting Degree-Aware Mixed-Precision Quantization"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2311.09775","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:c708d58908e7c5fa220459cc45b002d66ff912e04f2cb422327b8d111f3227bf","target":"record","created_at":"2026-07-05T07:13:32Z","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":"5192315f79fef0ddbc1c6f2e38e8db4fa34fee6f4e74e00ebb9716d0ef0078d7","cross_cats_sorted":["eess.SP"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AR","submitted_at":"2023-11-16T10:58:34Z","title_canon_sha256":"cd02a3156b311170b5c1faaa39444ae1b4742fc095e624951f775a310a4cb226"},"schema_version":"1.0","source":{"id":"2311.09775","kind":"arxiv","version":1}},"canonical_sha256":"a1607f5ea3d50c29974e57a15e1840ad80a78fd361ee38c5d5be2d380cdd7a25","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"a1607f5ea3d50c29974e57a15e1840ad80a78fd361ee38c5d5be2d380cdd7a25","first_computed_at":"2026-07-05T07:13:32.674691Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T07:13:32.674691Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"4n17xTBikPbpGSVNGkc9qhML/sz4BSTfCXSgapuofXWFYy/jbyYD+dtL5NPlf8hpZ4eHJ9SjJlDGEr/YsaMUAQ==","signature_status":"signed_v1","signed_at":"2026-07-05T07:13:32.675067Z","signed_message":"canonical_sha256_bytes"},"source_id":"2311.09775","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:c708d58908e7c5fa220459cc45b002d66ff912e04f2cb422327b8d111f3227bf","sha256:23e3bae2f169b7b6321482a0b06c447f51795e945941b87f8133f3e398f77c23"],"state_sha256":"4fd09017e609fe2a0dcf6cecd960880476778d9a7e2c875728062c806ca4d5f4"}