{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:KMJ7B3D7KXHELXPIKCBVWW2OAH","short_pith_number":"pith:KMJ7B3D7","schema_version":"1.0","canonical_sha256":"5313f0ec7f55ce45dde850835b5b4e01d23bbecdbc57fe85a9661cbc5c9bee72","source":{"kind":"arxiv","id":"2212.10200","version":1},"attestation_state":"computed","paper":{"title":"Redistribution of Weights and Activations for AdderNet Quantization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Chuanjian Liu, Enhua Wu, Haikang Diao, Kai Han, Ying Nie, Yunhe Wang","submitted_at":"2022-12-20T12:24:48Z","abstract_excerpt":"Adder Neural Network (AdderNet) provides a new way for developing energy-efficient neural networks by replacing the expensive multiplications in convolution with cheaper additions (i.e.l1-norm). To achieve higher hardware efficiency, it is necessary to further study the low-bit quantization of AdderNet. Due to the limitation that the commutative law in multiplication does not hold in l1-norm, the well-established quantization methods on convolutional networks cannot be applied on AdderNets. Thus, the existing AdderNet quantization techniques propose to use only one shared scale to quantize bot"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2212.10200","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2022-12-20T12:24:48Z","cross_cats_sorted":[],"title_canon_sha256":"bc0379c3016e73dda0892127762418da13c72857846ef1fb0c2fc5f82b008778","abstract_canon_sha256":"4ca1008714d506c8fdc08e634088174b5025963245c69e66e44bab614191285f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:27:05.104993Z","signature_b64":"ILtZuRhzt9gZZM3cBmRQGcZ0J+iDN2QDDekQfhDmxHIKNZesLbMgqCJBRk+zp8mUwve6VgiLBIyKQYd1DKTECw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5313f0ec7f55ce45dde850835b5b4e01d23bbecdbc57fe85a9661cbc5c9bee72","last_reissued_at":"2026-07-05T05:27:05.104617Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:27:05.104617Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Redistribution of Weights and Activations for AdderNet Quantization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Chuanjian Liu, Enhua Wu, Haikang Diao, Kai Han, Ying Nie, Yunhe Wang","submitted_at":"2022-12-20T12:24:48Z","abstract_excerpt":"Adder Neural Network (AdderNet) provides a new way for developing energy-efficient neural networks by replacing the expensive multiplications in convolution with cheaper additions (i.e.l1-norm). To achieve higher hardware efficiency, it is necessary to further study the low-bit quantization of AdderNet. Due to the limitation that the commutative law in multiplication does not hold in l1-norm, the well-established quantization methods on convolutional networks cannot be applied on AdderNets. Thus, the existing AdderNet quantization techniques propose to use only one shared scale to quantize bot"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2212.10200","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/2212.10200/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2212.10200","created_at":"2026-07-05T05:27:05.104669+00:00"},{"alias_kind":"arxiv_version","alias_value":"2212.10200v1","created_at":"2026-07-05T05:27:05.104669+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2212.10200","created_at":"2026-07-05T05:27:05.104669+00:00"},{"alias_kind":"pith_short_12","alias_value":"KMJ7B3D7KXHE","created_at":"2026-07-05T05:27:05.104669+00:00"},{"alias_kind":"pith_short_16","alias_value":"KMJ7B3D7KXHELXPI","created_at":"2026-07-05T05:27:05.104669+00:00"},{"alias_kind":"pith_short_8","alias_value":"KMJ7B3D7","created_at":"2026-07-05T05:27:05.104669+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/KMJ7B3D7KXHELXPIKCBVWW2OAH","json":"https://pith.science/pith/KMJ7B3D7KXHELXPIKCBVWW2OAH.json","graph_json":"https://pith.science/api/pith-number/KMJ7B3D7KXHELXPIKCBVWW2OAH/graph.json","events_json":"https://pith.science/api/pith-number/KMJ7B3D7KXHELXPIKCBVWW2OAH/events.json","paper":"https://pith.science/paper/KMJ7B3D7"},"agent_actions":{"view_html":"https://pith.science/pith/KMJ7B3D7KXHELXPIKCBVWW2OAH","download_json":"https://pith.science/pith/KMJ7B3D7KXHELXPIKCBVWW2OAH.json","view_paper":"https://pith.science/paper/KMJ7B3D7","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2212.10200&json=true","fetch_graph":"https://pith.science/api/pith-number/KMJ7B3D7KXHELXPIKCBVWW2OAH/graph.json","fetch_events":"https://pith.science/api/pith-number/KMJ7B3D7KXHELXPIKCBVWW2OAH/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/KMJ7B3D7KXHELXPIKCBVWW2OAH/action/timestamp_anchor","attest_storage":"https://pith.science/pith/KMJ7B3D7KXHELXPIKCBVWW2OAH/action/storage_attestation","attest_author":"https://pith.science/pith/KMJ7B3D7KXHELXPIKCBVWW2OAH/action/author_attestation","sign_citation":"https://pith.science/pith/KMJ7B3D7KXHELXPIKCBVWW2OAH/action/citation_signature","submit_replication":"https://pith.science/pith/KMJ7B3D7KXHELXPIKCBVWW2OAH/action/replication_record"}},"created_at":"2026-07-05T05:27:05.104669+00:00","updated_at":"2026-07-05T05:27:05.104669+00:00"}