{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:6W26OJRHLUCFE2AMTERPKULRVX","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":"e8c593ef7a3f4a564283ec98ce6caea17a298d2921e7ace48a7fdd949e910b36","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AR","submitted_at":"2025-04-19T02:51:45Z","title_canon_sha256":"74e0fa9571517e3ecb1ffe76edb106829709e1a5575ccb065c14e5cc7e629950"},"schema_version":"1.0","source":{"id":"2504.14152","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2504.14152","created_at":"2026-07-05T10:51:11Z"},{"alias_kind":"arxiv_version","alias_value":"2504.14152v1","created_at":"2026-07-05T10:51:11Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2504.14152","created_at":"2026-07-05T10:51:11Z"},{"alias_kind":"pith_short_12","alias_value":"6W26OJRHLUCF","created_at":"2026-07-05T10:51:11Z"},{"alias_kind":"pith_short_16","alias_value":"6W26OJRHLUCFE2AM","created_at":"2026-07-05T10:51:11Z"},{"alias_kind":"pith_short_8","alias_value":"6W26OJRH","created_at":"2026-07-05T10:51:11Z"}],"graph_snapshots":[{"event_id":"sha256:05920e5fdcecd0d717c328e3dd9efe96b99e72fd7ef80b7d623824dab98ed00d","target":"graph","created_at":"2026-07-05T10:51:11Z","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/2504.14152/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Quantization is a powerful tool to improve large language model (LLM) inference efficiency by utilizing more energy-efficient low-precision datapaths and reducing memory footprint. However, accurately quantizing LLM weights and activations to low precision is challenging without degrading model accuracy. We propose fine-grained mixed precision (FGMP) quantization, a post-training mixed-precision quantization hardware-software co-design methodology that maintains accuracy while quantizing the majority of weights and activations to reduced precision. Our work makes the following contributions: 1","authors_text":"Ben Keller, Brucek Khailany, Charbel Sakr, Coleman Hooper, Kurt Keutzer, Rangharajan Venkatesan, Sophia Shao","cross_cats":["cs.LG"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AR","submitted_at":"2025-04-19T02:51:45Z","title":"FGMP: Fine-Grained Mixed-Precision Weight and Activation Quantization for Hardware-Accelerated LLM Inference"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2504.14152","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":"sha256:3776ceffdbf1f03835ca8c0c1276fd93b5121383ab7b0b2b927b3b2a2dbe3b35","target":"record","created_at":"2026-07-05T10:51:11Z","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":"e8c593ef7a3f4a564283ec98ce6caea17a298d2921e7ace48a7fdd949e910b36","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AR","submitted_at":"2025-04-19T02:51:45Z","title_canon_sha256":"74e0fa9571517e3ecb1ffe76edb106829709e1a5575ccb065c14e5cc7e629950"},"schema_version":"1.0","source":{"id":"2504.14152","kind":"arxiv","version":1}},"canonical_sha256":"f5b5e726275d0452680c9922f55171adc2483d2369ecdf8792d507765db223ef","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"f5b5e726275d0452680c9922f55171adc2483d2369ecdf8792d507765db223ef","first_computed_at":"2026-07-05T10:51:11.837789Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:51:11.837789Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"IRISfmPchh8kWnl4vjhsgg5zjqOkGpJPxS+B8s+kdXFjOxzqJOPJv6vm9fuYbGYg2WveJW0LUqZVMvcn4+GxCw==","signature_status":"signed_v1","signed_at":"2026-07-05T10:51:11.838521Z","signed_message":"canonical_sha256_bytes"},"source_id":"2504.14152","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:3776ceffdbf1f03835ca8c0c1276fd93b5121383ab7b0b2b927b3b2a2dbe3b35","sha256:05920e5fdcecd0d717c328e3dd9efe96b99e72fd7ef80b7d623824dab98ed00d"],"state_sha256":"e7f267c70cb5e505dec1418498163aff70df4df5c9c4f8fd7011c9edb4461baf"}