{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:XJWGUCSEPVEQ4CNBF4ZMVFA2FV","short_pith_number":"pith:XJWGUCSE","schema_version":"1.0","canonical_sha256":"ba6c6a0a447d490e09a12f32ca941a2d625bf533113e30d09215dfa718218f60","source":{"kind":"arxiv","id":"1811.02636","version":4},"attestation_state":"computed","paper":{"title":"A mixed signal architecture for convolutional neural networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Andras Horvath, Azad Naeemi, Chenyun Pan, John McGuiness, Michael Niemier, Qiuwen Lou, X. Sharon Hu","submitted_at":"2018-10-30T18:51:57Z","abstract_excerpt":"Deep neural network (DNN) accelerators with improved energy and delay are desirable for meeting the requirements of hardware targeted for IoT and edge computing systems. Convolutional neural networks (CoNNs) belong to one of the most popular types of DNN architectures. This paper presents the design and evaluation of an accelerator for CoNNs. The system-level architecture is based on mixed-signal, cellular neural networks (CeNNs). Specifically, we present (i) the implementation of different layers, including convolution, ReLU, and pooling, in a CoNN using CeNN, (ii) modified CoNN structures wi"},"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":"1811.02636","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-10-30T18:51:57Z","cross_cats_sorted":[],"title_canon_sha256":"311a42c7aa81d6161461091509840455752f2297d701e2a290121ecfbb316463","abstract_canon_sha256":"2d1aa36dbf9cd4c995ba012d4b16ea6c1d80e3bebdc4a3377ba964437a06c1d4"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:47:08.598165Z","signature_b64":"RbvZoT0TLpXfXoaHEK99dvU/56PUtV+7xeG0LHIvPRFuatPdImZpgI7fZUxdVVXMC8FX0iqHFlG3edKfPY//Dg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ba6c6a0a447d490e09a12f32ca941a2d625bf533113e30d09215dfa718218f60","last_reissued_at":"2026-05-17T23:47:08.597577Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:47:08.597577Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A mixed signal architecture for convolutional neural networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Andras Horvath, Azad Naeemi, Chenyun Pan, John McGuiness, Michael Niemier, Qiuwen Lou, X. Sharon Hu","submitted_at":"2018-10-30T18:51:57Z","abstract_excerpt":"Deep neural network (DNN) accelerators with improved energy and delay are desirable for meeting the requirements of hardware targeted for IoT and edge computing systems. Convolutional neural networks (CoNNs) belong to one of the most popular types of DNN architectures. This paper presents the design and evaluation of an accelerator for CoNNs. The system-level architecture is based on mixed-signal, cellular neural networks (CeNNs). Specifically, we present (i) the implementation of different layers, including convolution, ReLU, and pooling, in a CoNN using CeNN, (ii) modified CoNN structures wi"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1811.02636","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":"1811.02636","created_at":"2026-05-17T23:47:08.597668+00:00"},{"alias_kind":"arxiv_version","alias_value":"1811.02636v4","created_at":"2026-05-17T23:47:08.597668+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1811.02636","created_at":"2026-05-17T23:47:08.597668+00:00"},{"alias_kind":"pith_short_12","alias_value":"XJWGUCSEPVEQ","created_at":"2026-05-18T12:33:01.666342+00:00"},{"alias_kind":"pith_short_16","alias_value":"XJWGUCSEPVEQ4CNB","created_at":"2026-05-18T12:33:01.666342+00:00"},{"alias_kind":"pith_short_8","alias_value":"XJWGUCSE","created_at":"2026-05-18T12:33:01.666342+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/XJWGUCSEPVEQ4CNBF4ZMVFA2FV","json":"https://pith.science/pith/XJWGUCSEPVEQ4CNBF4ZMVFA2FV.json","graph_json":"https://pith.science/api/pith-number/XJWGUCSEPVEQ4CNBF4ZMVFA2FV/graph.json","events_json":"https://pith.science/api/pith-number/XJWGUCSEPVEQ4CNBF4ZMVFA2FV/events.json","paper":"https://pith.science/paper/XJWGUCSE"},"agent_actions":{"view_html":"https://pith.science/pith/XJWGUCSEPVEQ4CNBF4ZMVFA2FV","download_json":"https://pith.science/pith/XJWGUCSEPVEQ4CNBF4ZMVFA2FV.json","view_paper":"https://pith.science/paper/XJWGUCSE","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1811.02636&json=true","fetch_graph":"https://pith.science/api/pith-number/XJWGUCSEPVEQ4CNBF4ZMVFA2FV/graph.json","fetch_events":"https://pith.science/api/pith-number/XJWGUCSEPVEQ4CNBF4ZMVFA2FV/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/XJWGUCSEPVEQ4CNBF4ZMVFA2FV/action/timestamp_anchor","attest_storage":"https://pith.science/pith/XJWGUCSEPVEQ4CNBF4ZMVFA2FV/action/storage_attestation","attest_author":"https://pith.science/pith/XJWGUCSEPVEQ4CNBF4ZMVFA2FV/action/author_attestation","sign_citation":"https://pith.science/pith/XJWGUCSEPVEQ4CNBF4ZMVFA2FV/action/citation_signature","submit_replication":"https://pith.science/pith/XJWGUCSEPVEQ4CNBF4ZMVFA2FV/action/replication_record"}},"created_at":"2026-05-17T23:47:08.597668+00:00","updated_at":"2026-05-17T23:47:08.597668+00:00"}