{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:65DUON2BTWOVDCDR625JONY7HM","short_pith_number":"pith:65DUON2B","schema_version":"1.0","canonical_sha256":"f7474737419d9d518871f6ba97371f3b3b050f1abfb4745731ceaa1ef167575c","source":{"kind":"arxiv","id":"1710.09012","version":1},"attestation_state":"computed","paper":{"title":"An Energy-Efficient Mixed-Signal Neuron for Inherently Error-Resilient Neuromorphic Systems","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.AR"],"primary_cat":"cs.ET","authors_text":"Baibhab Chatterjee, Kaushik Roy, Priyadarshini Panda, Shovan Maity, Shreyas Sen","submitted_at":"2017-10-24T22:43:16Z","abstract_excerpt":"This work presents the design and analysis of a mixed-signal neuron (MS-N) for convolutional neural networks (CNN) and compares its performance with a digital neuron (Dig-N) in terms of operating frequency, power and noise. The circuit-level implementation of the MS-N in 65 nm CMOS technology exhibits 2-3 orders of magnitude better energy-efficiency over Dig-N for neuromorphic computing applications - especially at low frequencies due to the high leakage currents from many transistors in Dig-N. The inherent error-resiliency of CNN is exploited to handle the thermal and flicker noise of MS-N. A"},"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":"1710.09012","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.ET","submitted_at":"2017-10-24T22:43:16Z","cross_cats_sorted":["cs.AI","cs.AR"],"title_canon_sha256":"bc9b3f72595770e3e475c37b2d37c6a897f054c45e95b73b1210c42f61ffa7f9","abstract_canon_sha256":"c010b0eedbbddcff1f9f52522a98e0c1f4200aa2334013498cd42d6118beb86a"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:16:50.497428Z","signature_b64":"zZqhDhLaiYv/FRPqLlumMDJVuQo0ec+xYMynDOjdXrVlg7udov7oGkml2cJR5fETa2HDyt7xiQCM7bxsxwhhDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f7474737419d9d518871f6ba97371f3b3b050f1abfb4745731ceaa1ef167575c","last_reissued_at":"2026-05-18T00:16:50.496627Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:16:50.496627Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"An Energy-Efficient Mixed-Signal Neuron for Inherently Error-Resilient Neuromorphic Systems","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.AR"],"primary_cat":"cs.ET","authors_text":"Baibhab Chatterjee, Kaushik Roy, Priyadarshini Panda, Shovan Maity, Shreyas Sen","submitted_at":"2017-10-24T22:43:16Z","abstract_excerpt":"This work presents the design and analysis of a mixed-signal neuron (MS-N) for convolutional neural networks (CNN) and compares its performance with a digital neuron (Dig-N) in terms of operating frequency, power and noise. The circuit-level implementation of the MS-N in 65 nm CMOS technology exhibits 2-3 orders of magnitude better energy-efficiency over Dig-N for neuromorphic computing applications - especially at low frequencies due to the high leakage currents from many transistors in Dig-N. The inherent error-resiliency of CNN is exploited to handle the thermal and flicker noise of MS-N. A"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1710.09012","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":"1710.09012","created_at":"2026-05-18T00:16:50.496738+00:00"},{"alias_kind":"arxiv_version","alias_value":"1710.09012v1","created_at":"2026-05-18T00:16:50.496738+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1710.09012","created_at":"2026-05-18T00:16:50.496738+00:00"},{"alias_kind":"pith_short_12","alias_value":"65DUON2BTWOV","created_at":"2026-05-18T12:31:03.183658+00:00"},{"alias_kind":"pith_short_16","alias_value":"65DUON2BTWOVDCDR","created_at":"2026-05-18T12:31:03.183658+00:00"},{"alias_kind":"pith_short_8","alias_value":"65DUON2B","created_at":"2026-05-18T12:31:03.183658+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/65DUON2BTWOVDCDR625JONY7HM","json":"https://pith.science/pith/65DUON2BTWOVDCDR625JONY7HM.json","graph_json":"https://pith.science/api/pith-number/65DUON2BTWOVDCDR625JONY7HM/graph.json","events_json":"https://pith.science/api/pith-number/65DUON2BTWOVDCDR625JONY7HM/events.json","paper":"https://pith.science/paper/65DUON2B"},"agent_actions":{"view_html":"https://pith.science/pith/65DUON2BTWOVDCDR625JONY7HM","download_json":"https://pith.science/pith/65DUON2BTWOVDCDR625JONY7HM.json","view_paper":"https://pith.science/paper/65DUON2B","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1710.09012&json=true","fetch_graph":"https://pith.science/api/pith-number/65DUON2BTWOVDCDR625JONY7HM/graph.json","fetch_events":"https://pith.science/api/pith-number/65DUON2BTWOVDCDR625JONY7HM/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/65DUON2BTWOVDCDR625JONY7HM/action/timestamp_anchor","attest_storage":"https://pith.science/pith/65DUON2BTWOVDCDR625JONY7HM/action/storage_attestation","attest_author":"https://pith.science/pith/65DUON2BTWOVDCDR625JONY7HM/action/author_attestation","sign_citation":"https://pith.science/pith/65DUON2BTWOVDCDR625JONY7HM/action/citation_signature","submit_replication":"https://pith.science/pith/65DUON2BTWOVDCDR625JONY7HM/action/replication_record"}},"created_at":"2026-05-18T00:16:50.496738+00:00","updated_at":"2026-05-18T00:16:50.496738+00:00"}