{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:NLIVDNNOBHUBZMOLB3U2PKLWZF","short_pith_number":"pith:NLIVDNNO","schema_version":"1.0","canonical_sha256":"6ad151b5ae09e81cb1cb0ee9a7a976c967cbe88bf64c631c85c0de9e6823b3ac","source":{"kind":"arxiv","id":"1802.02608","version":1},"attestation_state":"computed","paper":{"title":"Deep Versus Wide Convolutional Neural Networks for Object Recognition on Neuromorphic System","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Chris Yakopcic, Md Nayim Rahman, Md Zahangir Alom, Tarek M. Taha, Theodore Josue, Will Mitchell","submitted_at":"2018-02-07T19:32:40Z","abstract_excerpt":"In the last decade, special purpose computing systems, such as Neuromorphic computing, have become very popular in the field of computer vision and machine learning for classification tasks. In 2015, IBM's released the TrueNorth Neuromorphic system, kick-starting a new era of Neuromorphic computing. Alternatively, Deep Learning approaches such as Deep Convolutional Neural Networks (DCNN) show almost human-level accuracies for detection and classification tasks. IBM's 2016 release of a deep learning framework for DCNNs, called Energy Efficient Deep Neuromorphic Networks (Eedn). Eedn shows promi"},"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":"1802.02608","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2018-02-07T19:32:40Z","cross_cats_sorted":[],"title_canon_sha256":"fc4ad607de57ea5a497ec3a95bb6bdcdcdd3717855a42bd5c651d26539582651","abstract_canon_sha256":"3dc4ffce1b392681e035a713dc2cabb22b5c5a28349dc293bb6b2c27aacc8057"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:24:03.561340Z","signature_b64":"I//A4TEV22+KXAtClqwnXx1ocHX/QLu1ve8Qjbt2IiAp+pbMvIGQRUPz9Cop24XllRAB2zSYb989RhU2yYGcDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6ad151b5ae09e81cb1cb0ee9a7a976c967cbe88bf64c631c85c0de9e6823b3ac","last_reissued_at":"2026-05-18T00:24:03.560935Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:24:03.560935Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Deep Versus Wide Convolutional Neural Networks for Object Recognition on Neuromorphic System","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Chris Yakopcic, Md Nayim Rahman, Md Zahangir Alom, Tarek M. Taha, Theodore Josue, Will Mitchell","submitted_at":"2018-02-07T19:32:40Z","abstract_excerpt":"In the last decade, special purpose computing systems, such as Neuromorphic computing, have become very popular in the field of computer vision and machine learning for classification tasks. In 2015, IBM's released the TrueNorth Neuromorphic system, kick-starting a new era of Neuromorphic computing. Alternatively, Deep Learning approaches such as Deep Convolutional Neural Networks (DCNN) show almost human-level accuracies for detection and classification tasks. IBM's 2016 release of a deep learning framework for DCNNs, called Energy Efficient Deep Neuromorphic Networks (Eedn). Eedn shows promi"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1802.02608","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":""},"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":"1802.02608","created_at":"2026-05-18T00:24:03.560996+00:00"},{"alias_kind":"arxiv_version","alias_value":"1802.02608v1","created_at":"2026-05-18T00:24:03.560996+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1802.02608","created_at":"2026-05-18T00:24:03.560996+00:00"},{"alias_kind":"pith_short_12","alias_value":"NLIVDNNOBHUB","created_at":"2026-05-18T12:32:40.477152+00:00"},{"alias_kind":"pith_short_16","alias_value":"NLIVDNNOBHUBZMOL","created_at":"2026-05-18T12:32:40.477152+00:00"},{"alias_kind":"pith_short_8","alias_value":"NLIVDNNO","created_at":"2026-05-18T12:32:40.477152+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/NLIVDNNOBHUBZMOLB3U2PKLWZF","json":"https://pith.science/pith/NLIVDNNOBHUBZMOLB3U2PKLWZF.json","graph_json":"https://pith.science/api/pith-number/NLIVDNNOBHUBZMOLB3U2PKLWZF/graph.json","events_json":"https://pith.science/api/pith-number/NLIVDNNOBHUBZMOLB3U2PKLWZF/events.json","paper":"https://pith.science/paper/NLIVDNNO"},"agent_actions":{"view_html":"https://pith.science/pith/NLIVDNNOBHUBZMOLB3U2PKLWZF","download_json":"https://pith.science/pith/NLIVDNNOBHUBZMOLB3U2PKLWZF.json","view_paper":"https://pith.science/paper/NLIVDNNO","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1802.02608&json=true","fetch_graph":"https://pith.science/api/pith-number/NLIVDNNOBHUBZMOLB3U2PKLWZF/graph.json","fetch_events":"https://pith.science/api/pith-number/NLIVDNNOBHUBZMOLB3U2PKLWZF/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/NLIVDNNOBHUBZMOLB3U2PKLWZF/action/timestamp_anchor","attest_storage":"https://pith.science/pith/NLIVDNNOBHUBZMOLB3U2PKLWZF/action/storage_attestation","attest_author":"https://pith.science/pith/NLIVDNNOBHUBZMOLB3U2PKLWZF/action/author_attestation","sign_citation":"https://pith.science/pith/NLIVDNNOBHUBZMOLB3U2PKLWZF/action/citation_signature","submit_replication":"https://pith.science/pith/NLIVDNNOBHUBZMOLB3U2PKLWZF/action/replication_record"}},"created_at":"2026-05-18T00:24:03.560996+00:00","updated_at":"2026-05-18T00:24:03.560996+00:00"}