{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:TRKB3S4ECBCQ23CIFA2QNMQVSZ","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":"aa0756fdfa1aa747eeacd8e3ba143d70d2321a0ad2cef4faf0b13cf334d14497","cross_cats_sorted":["cs.CV"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-01-30T23:26:33Z","title_canon_sha256":"5e8bc756d813f02f95bb4fec4ac0268cd05d9ef76d68b2e6a9a3f6957b61b047"},"schema_version":"1.0","source":{"id":"2301.13330","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2301.13330","created_at":"2026-07-05T07:32:16Z"},{"alias_kind":"arxiv_version","alias_value":"2301.13330v2","created_at":"2026-07-05T07:32:16Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2301.13330","created_at":"2026-07-05T07:32:16Z"},{"alias_kind":"pith_short_12","alias_value":"TRKB3S4ECBCQ","created_at":"2026-07-05T07:32:16Z"},{"alias_kind":"pith_short_16","alias_value":"TRKB3S4ECBCQ23CI","created_at":"2026-07-05T07:32:16Z"},{"alias_kind":"pith_short_8","alias_value":"TRKB3S4E","created_at":"2026-07-05T07:32:16Z"}],"graph_snapshots":[{"event_id":"sha256:01027b9b8b21488cacd9801b325d00e54a2bc882e5d9655476e979398f532ed2","target":"graph","created_at":"2026-07-05T07:32:16Z","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/2301.13330/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"For efficient neural network inference, it is desirable to achieve state-of-the-art accuracy with the simplest networks requiring the least computation, memory, and power. Quantizing networks to lower precision is a powerful technique for simplifying networks. As each layer of a network may have different sensitivity to quantization, mixed precision quantization methods selectively tune the precision of individual layers to achieve a minimum drop in task performance (e.g., accuracy). To estimate the impact of layer precision choice on task performance, two methods are introduced: i) Entropy Ap","authors_text":"Deepika Bablani, Dharmendra S. Modha, Jeffrey L. McKinstry, Rathinakumar Appuswamy, Steven K. Esser","cross_cats":["cs.CV"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-01-30T23:26:33Z","title":"Efficient and Effective Methods for Mixed Precision Neural Network Quantization for Faster, Energy-efficient Inference"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2301.13330","kind":"arxiv","version":2},"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":"sha256:3d232cbe90ff3e2a21aae3d0ba64cd16a7507a301abe4f2522b7154cb19f828d","target":"record","created_at":"2026-07-05T07:32:16Z","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":"aa0756fdfa1aa747eeacd8e3ba143d70d2321a0ad2cef4faf0b13cf334d14497","cross_cats_sorted":["cs.CV"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-01-30T23:26:33Z","title_canon_sha256":"5e8bc756d813f02f95bb4fec4ac0268cd05d9ef76d68b2e6a9a3f6957b61b047"},"schema_version":"1.0","source":{"id":"2301.13330","kind":"arxiv","version":2}},"canonical_sha256":"9c541dcb8410450d6c48283506b215965b35bd9eaa483c90f339acb9cbc6d470","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"9c541dcb8410450d6c48283506b215965b35bd9eaa483c90f339acb9cbc6d470","first_computed_at":"2026-07-05T07:32:16.785003Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T07:32:16.785003Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"f8lea56cm2Q5I39rIIm1OKJeLOu3+H5qbUfc6ZQzNjoaRShNRQYPYPS6UDIUiy7ocaYiCRS7IZctX46qGOseBw==","signature_status":"signed_v1","signed_at":"2026-07-05T07:32:16.785449Z","signed_message":"canonical_sha256_bytes"},"source_id":"2301.13330","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:3d232cbe90ff3e2a21aae3d0ba64cd16a7507a301abe4f2522b7154cb19f828d","sha256:01027b9b8b21488cacd9801b325d00e54a2bc882e5d9655476e979398f532ed2"],"state_sha256":"1d31d9b12e2db2bc37eed63759247e6e7034e101979263c5948b5bb755665e96"}