{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:QJLRWSHW6U5C6K7GDIES6BNFDF","short_pith_number":"pith:QJLRWSHW","schema_version":"1.0","canonical_sha256":"82571b48f6f53a2f2be61a092f05a5195759441247cfa605baa2a1f0b47db045","source":{"kind":"arxiv","id":"1709.06614","version":4},"attestation_state":"computed","paper":{"title":"An Analog Neural Network Computing Engine using CMOS-Compatible Charge-Trap-Transistor (CTT)","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AR","cs.LG"],"primary_cat":"cs.ET","authors_text":"Boyu Hu, Jieqiong Du, Junjie Su, Li Du, Mau-Chung Frank Chang, Mingzhe Jiang, Subramanian S. Iyer, Xiaoliang Chen, X. Shawn Wang, Xuefeng Gu, Yuan Du","submitted_at":"2017-09-19T19:09:16Z","abstract_excerpt":"An analog neural network computing engine based on CMOS-compatible charge-trap transistor (CTT) is proposed in this paper. CTT devices are used as analog multipliers. Compared to digital multipliers, CTT-based analog multiplier shows significant area and power reduction. The proposed computing engine is composed of a scalable CTT multiplier array and energy efficient analog-digital interfaces. Through implementing the sequential analog fabric (SAF), the engine mixed-signal interfaces are simplified and hardware overhead remains constant regardless of the size of the array. A proof-of-concept 7"},"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":"1709.06614","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.ET","submitted_at":"2017-09-19T19:09:16Z","cross_cats_sorted":["cs.AR","cs.LG"],"title_canon_sha256":"4c07e1bea1400838de49b86d601e9d622fb759482d1dc0586f1e0392dec61516","abstract_canon_sha256":"23775a7ad8dc76d5dec5cf4376ddbf2fb204db684489f59347af6b6f2e9a4bf4"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:08:31.385215Z","signature_b64":"n9jibur5Ze6auafUQbeBIIh8AItRiD4Y4udtO6OoRi1ekPzUwZaL10svdeVO6DJxdywGg7FmgyWiYyFD1GLjBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"82571b48f6f53a2f2be61a092f05a5195759441247cfa605baa2a1f0b47db045","last_reissued_at":"2026-05-18T00:08:31.384617Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:08:31.384617Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"An Analog Neural Network Computing Engine using CMOS-Compatible Charge-Trap-Transistor (CTT)","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AR","cs.LG"],"primary_cat":"cs.ET","authors_text":"Boyu Hu, Jieqiong Du, Junjie Su, Li Du, Mau-Chung Frank Chang, Mingzhe Jiang, Subramanian S. Iyer, Xiaoliang Chen, X. Shawn Wang, Xuefeng Gu, Yuan Du","submitted_at":"2017-09-19T19:09:16Z","abstract_excerpt":"An analog neural network computing engine based on CMOS-compatible charge-trap transistor (CTT) is proposed in this paper. CTT devices are used as analog multipliers. Compared to digital multipliers, CTT-based analog multiplier shows significant area and power reduction. The proposed computing engine is composed of a scalable CTT multiplier array and energy efficient analog-digital interfaces. Through implementing the sequential analog fabric (SAF), the engine mixed-signal interfaces are simplified and hardware overhead remains constant regardless of the size of the array. A proof-of-concept 7"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1709.06614","kind":"arxiv","version":4},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"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":"1709.06614","created_at":"2026-05-18T00:08:31.384715+00:00"},{"alias_kind":"arxiv_version","alias_value":"1709.06614v4","created_at":"2026-05-18T00:08:31.384715+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1709.06614","created_at":"2026-05-18T00:08:31.384715+00:00"},{"alias_kind":"pith_short_12","alias_value":"QJLRWSHW6U5C","created_at":"2026-05-18T12:31:39.905425+00:00"},{"alias_kind":"pith_short_16","alias_value":"QJLRWSHW6U5C6K7G","created_at":"2026-05-18T12:31:39.905425+00:00"},{"alias_kind":"pith_short_8","alias_value":"QJLRWSHW","created_at":"2026-05-18T12:31:39.905425+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/QJLRWSHW6U5C6K7GDIES6BNFDF","json":"https://pith.science/pith/QJLRWSHW6U5C6K7GDIES6BNFDF.json","graph_json":"https://pith.science/api/pith-number/QJLRWSHW6U5C6K7GDIES6BNFDF/graph.json","events_json":"https://pith.science/api/pith-number/QJLRWSHW6U5C6K7GDIES6BNFDF/events.json","paper":"https://pith.science/paper/QJLRWSHW"},"agent_actions":{"view_html":"https://pith.science/pith/QJLRWSHW6U5C6K7GDIES6BNFDF","download_json":"https://pith.science/pith/QJLRWSHW6U5C6K7GDIES6BNFDF.json","view_paper":"https://pith.science/paper/QJLRWSHW","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1709.06614&json=true","fetch_graph":"https://pith.science/api/pith-number/QJLRWSHW6U5C6K7GDIES6BNFDF/graph.json","fetch_events":"https://pith.science/api/pith-number/QJLRWSHW6U5C6K7GDIES6BNFDF/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/QJLRWSHW6U5C6K7GDIES6BNFDF/action/timestamp_anchor","attest_storage":"https://pith.science/pith/QJLRWSHW6U5C6K7GDIES6BNFDF/action/storage_attestation","attest_author":"https://pith.science/pith/QJLRWSHW6U5C6K7GDIES6BNFDF/action/author_attestation","sign_citation":"https://pith.science/pith/QJLRWSHW6U5C6K7GDIES6BNFDF/action/citation_signature","submit_replication":"https://pith.science/pith/QJLRWSHW6U5C6K7GDIES6BNFDF/action/replication_record"}},"created_at":"2026-05-18T00:08:31.384715+00:00","updated_at":"2026-05-18T00:08:31.384715+00:00"}