{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:6TTFCFPZNETNEUDBSW7E26JCSK","short_pith_number":"pith:6TTFCFPZ","schema_version":"1.0","canonical_sha256":"f4e65115f96926d2506195be4d792292969dde2f2c08c300a8e75941c5364bb6","source":{"kind":"arxiv","id":"2508.21524","version":1},"attestation_state":"computed","paper":{"title":"Binary Weight Multi-Bit Activation Quantization for Compute-in-Memory CNN Accelerators","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.AR","authors_text":"Ngai Wong, Wenyong Zhou, Yuan Ren, Zhengwu Liu","submitted_at":"2025-08-29T11:24:24Z","abstract_excerpt":"Compute-in-memory (CIM) accelerators have emerged as a promising way for enhancing the energy efficiency of convolutional neural networks (CNNs). Deploying CNNs on CIM platforms generally requires quantization of network weights and activations to meet hardware constraints. However, existing approaches either prioritize hardware efficiency with binary weight and activation quantization at the cost of accuracy, or utilize multi-bit weights and activations for greater accuracy but limited efficiency. In this paper, we introduce a novel binary weight multi-bit activation (BWMA) method for CNNs on"},"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":"2508.21524","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AR","submitted_at":"2025-08-29T11:24:24Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"24e91038f7fcc2b3e21b987621f88d012b9a12c27e3db5e4c028edb62dad557a","abstract_canon_sha256":"39c01c230633d645fc7ce4367718c6f81ca4ba317383d97ed7c5b2a8d0285618"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T12:01:36.103547Z","signature_b64":"L6u6pR/6TGaCdKOD5fzG8GAY5hUDKLyyKs6s/GTjkEDShe3UJVujzq1iymLRKpqa1XroX+Or7OUEITDz+Os1BA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f4e65115f96926d2506195be4d792292969dde2f2c08c300a8e75941c5364bb6","last_reissued_at":"2026-07-05T12:01:36.103124Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T12:01:36.103124Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Binary Weight Multi-Bit Activation Quantization for Compute-in-Memory CNN Accelerators","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.AR","authors_text":"Ngai Wong, Wenyong Zhou, Yuan Ren, Zhengwu Liu","submitted_at":"2025-08-29T11:24:24Z","abstract_excerpt":"Compute-in-memory (CIM) accelerators have emerged as a promising way for enhancing the energy efficiency of convolutional neural networks (CNNs). Deploying CNNs on CIM platforms generally requires quantization of network weights and activations to meet hardware constraints. However, existing approaches either prioritize hardware efficiency with binary weight and activation quantization at the cost of accuracy, or utilize multi-bit weights and activations for greater accuracy but limited efficiency. In this paper, we introduce a novel binary weight multi-bit activation (BWMA) method for CNNs on"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2508.21524","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/2508.21524/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":"2508.21524","created_at":"2026-07-05T12:01:36.103176+00:00"},{"alias_kind":"arxiv_version","alias_value":"2508.21524v1","created_at":"2026-07-05T12:01:36.103176+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2508.21524","created_at":"2026-07-05T12:01:36.103176+00:00"},{"alias_kind":"pith_short_12","alias_value":"6TTFCFPZNETN","created_at":"2026-07-05T12:01:36.103176+00:00"},{"alias_kind":"pith_short_16","alias_value":"6TTFCFPZNETNEUDB","created_at":"2026-07-05T12:01:36.103176+00:00"},{"alias_kind":"pith_short_8","alias_value":"6TTFCFPZ","created_at":"2026-07-05T12:01:36.103176+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/6TTFCFPZNETNEUDBSW7E26JCSK","json":"https://pith.science/pith/6TTFCFPZNETNEUDBSW7E26JCSK.json","graph_json":"https://pith.science/api/pith-number/6TTFCFPZNETNEUDBSW7E26JCSK/graph.json","events_json":"https://pith.science/api/pith-number/6TTFCFPZNETNEUDBSW7E26JCSK/events.json","paper":"https://pith.science/paper/6TTFCFPZ"},"agent_actions":{"view_html":"https://pith.science/pith/6TTFCFPZNETNEUDBSW7E26JCSK","download_json":"https://pith.science/pith/6TTFCFPZNETNEUDBSW7E26JCSK.json","view_paper":"https://pith.science/paper/6TTFCFPZ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2508.21524&json=true","fetch_graph":"https://pith.science/api/pith-number/6TTFCFPZNETNEUDBSW7E26JCSK/graph.json","fetch_events":"https://pith.science/api/pith-number/6TTFCFPZNETNEUDBSW7E26JCSK/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/6TTFCFPZNETNEUDBSW7E26JCSK/action/timestamp_anchor","attest_storage":"https://pith.science/pith/6TTFCFPZNETNEUDBSW7E26JCSK/action/storage_attestation","attest_author":"https://pith.science/pith/6TTFCFPZNETNEUDBSW7E26JCSK/action/author_attestation","sign_citation":"https://pith.science/pith/6TTFCFPZNETNEUDBSW7E26JCSK/action/citation_signature","submit_replication":"https://pith.science/pith/6TTFCFPZNETNEUDBSW7E26JCSK/action/replication_record"}},"created_at":"2026-07-05T12:01:36.103176+00:00","updated_at":"2026-07-05T12:01:36.103176+00:00"}