{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2021:5YLGAVGW73KT7L3U2XU5CCYRDP","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":"2d22d613195503f17aa74909a9c2ddb67ed79f06a442dd4aed4809fe45db6430","cross_cats_sorted":["cs.PF"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2021-01-13T00:34:04Z","title_canon_sha256":"de471a0711565a4e5edbe71331fc1bc17fb2b78b432ab4cd9e4622500c0f3512"},"schema_version":"1.0","source":{"id":"2101.05615","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2101.05615","created_at":"2026-07-05T02:06:57Z"},{"alias_kind":"arxiv_version","alias_value":"2101.05615v1","created_at":"2026-07-05T02:06:57Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2101.05615","created_at":"2026-07-05T02:06:57Z"},{"alias_kind":"pith_short_12","alias_value":"5YLGAVGW73KT","created_at":"2026-07-05T02:06:57Z"},{"alias_kind":"pith_short_16","alias_value":"5YLGAVGW73KT7L3U","created_at":"2026-07-05T02:06:57Z"},{"alias_kind":"pith_short_8","alias_value":"5YLGAVGW","created_at":"2026-07-05T02:06:57Z"}],"graph_snapshots":[{"event_id":"sha256:d959a7f3582b6e7a608c305fbe6d3d0343396f0e328293c1593de3c8a2fe3b7e","target":"graph","created_at":"2026-07-05T02:06:57Z","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/2101.05615/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Deep learning models typically use single-precision (FP32) floating point data types for representing activations and weights, but a slew of recent research work has shown that computations with reduced-precision data types (FP16, 16-bit integers, 8-bit integers or even 4- or 2-bit integers) are enough to achieve same accuracy as FP32 and are much more efficient. Therefore, we designed fbgemm, a high-performance kernel library, from ground up to perform high-performance quantized inference on current generation CPUs. fbgemm achieves efficiency by fusing common quantization operations with a hi","authors_text":"Daya Khudia, Haixin Liu, Jianyu Huang, Jongsoo Park, Mikhail Smelyanskiy, Protonu Basu, Summer Deng","cross_cats":["cs.PF"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2021-01-13T00:34:04Z","title":"FBGEMM: Enabling High-Performance Low-Precision Deep Learning Inference"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2101.05615","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":"sha256:1268a236a6869082869f097fdb1c1ad770623396bfaca5292f237f28d9b573cb","target":"record","created_at":"2026-07-05T02:06:57Z","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":"2d22d613195503f17aa74909a9c2ddb67ed79f06a442dd4aed4809fe45db6430","cross_cats_sorted":["cs.PF"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2021-01-13T00:34:04Z","title_canon_sha256":"de471a0711565a4e5edbe71331fc1bc17fb2b78b432ab4cd9e4622500c0f3512"},"schema_version":"1.0","source":{"id":"2101.05615","kind":"arxiv","version":1}},"canonical_sha256":"ee166054d6fed53faf74d5e9d10b111bc1d9aae2df0fa76e1528839d66af54db","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"ee166054d6fed53faf74d5e9d10b111bc1d9aae2df0fa76e1528839d66af54db","first_computed_at":"2026-07-05T02:06:57.527639Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T02:06:57.527639Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"HIr2f7t4iNfaatFgr4mbbfaHd5TIZMLjtub5IJf5oCIRq1qL0A9TCig/BHVAWiZVhl5NCHt5xr1ULIECWfRbDQ==","signature_status":"signed_v1","signed_at":"2026-07-05T02:06:57.528091Z","signed_message":"canonical_sha256_bytes"},"source_id":"2101.05615","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:1268a236a6869082869f097fdb1c1ad770623396bfaca5292f237f28d9b573cb","sha256:d959a7f3582b6e7a608c305fbe6d3d0343396f0e328293c1593de3c8a2fe3b7e"],"state_sha256":"f742112ce28e649cfb49f1ef2dfe9255dfddf713fb0a983a56897c401fb47497"}