{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2016:QA2PJTM42GWGGSDFA7WZAFGHMD","short_pith_number":"pith:QA2PJTM4","schema_version":"1.0","canonical_sha256":"8034f4cd9cd1ac63486507ed9014c760e21abb8e9d63cea5fcac9fb362c92f7d","source":{"kind":"arxiv","id":"1602.08557","version":1},"attestation_state":"computed","paper":{"title":"Multiplier-less Artificial Neurons Exploiting Error Resiliency for Energy-Efficient Neural Computing","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.NE","authors_text":"Anand Raghunathan, Kaushik Roy, Swagath Venkataramani, Syed Shakib Sarwar","submitted_at":"2016-02-27T05:37:44Z","abstract_excerpt":"Large-scale artificial neural networks have shown significant promise in addressing a wide range of classification and recognition applications. However, their large computational requirements stretch the capabilities of computing platforms. The fundamental components of these neural networks are the neurons and its synapses. The core of a digital hardware neuron consists of multiplier, accumulator and activation function. Multipliers consume most of the processing energy in the digital neurons, and thereby in the hardware implementations of artificial neural networks. We propose an approximat"},"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":"1602.08557","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NE","submitted_at":"2016-02-27T05:37:44Z","cross_cats_sorted":[],"title_canon_sha256":"9a262567cb54ac40efaaf7339811a6159af2a9c30b53c6299299b7cf1ed679fb","abstract_canon_sha256":"9705ad4a35784e2b42ae816ea9609fa7c8ab006537f9f138039b1e8a37b976bf"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:30:55.507945Z","signature_b64":"bKu8uoTy3tEZ4sbq9MyMHSCJ9zhQ21W0i59qo8J/AzYk5JmtJhnx+1bmLUjOiouZRLQ4G+fZWcSYHdygO70HAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8034f4cd9cd1ac63486507ed9014c760e21abb8e9d63cea5fcac9fb362c92f7d","last_reissued_at":"2026-05-18T00:30:55.507385Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:30:55.507385Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Multiplier-less Artificial Neurons Exploiting Error Resiliency for Energy-Efficient Neural Computing","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.NE","authors_text":"Anand Raghunathan, Kaushik Roy, Swagath Venkataramani, Syed Shakib Sarwar","submitted_at":"2016-02-27T05:37:44Z","abstract_excerpt":"Large-scale artificial neural networks have shown significant promise in addressing a wide range of classification and recognition applications. However, their large computational requirements stretch the capabilities of computing platforms. The fundamental components of these neural networks are the neurons and its synapses. The core of a digital hardware neuron consists of multiplier, accumulator and activation function. Multipliers consume most of the processing energy in the digital neurons, and thereby in the hardware implementations of artificial neural networks. We propose an approximat"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1602.08557","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":"1602.08557","created_at":"2026-05-18T00:30:55.507487+00:00"},{"alias_kind":"arxiv_version","alias_value":"1602.08557v1","created_at":"2026-05-18T00:30:55.507487+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1602.08557","created_at":"2026-05-18T00:30:55.507487+00:00"},{"alias_kind":"pith_short_12","alias_value":"QA2PJTM42GWG","created_at":"2026-05-18T12:30:39.010887+00:00"},{"alias_kind":"pith_short_16","alias_value":"QA2PJTM42GWGGSDF","created_at":"2026-05-18T12:30:39.010887+00:00"},{"alias_kind":"pith_short_8","alias_value":"QA2PJTM4","created_at":"2026-05-18T12:30:39.010887+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/QA2PJTM42GWGGSDFA7WZAFGHMD","json":"https://pith.science/pith/QA2PJTM42GWGGSDFA7WZAFGHMD.json","graph_json":"https://pith.science/api/pith-number/QA2PJTM42GWGGSDFA7WZAFGHMD/graph.json","events_json":"https://pith.science/api/pith-number/QA2PJTM42GWGGSDFA7WZAFGHMD/events.json","paper":"https://pith.science/paper/QA2PJTM4"},"agent_actions":{"view_html":"https://pith.science/pith/QA2PJTM42GWGGSDFA7WZAFGHMD","download_json":"https://pith.science/pith/QA2PJTM42GWGGSDFA7WZAFGHMD.json","view_paper":"https://pith.science/paper/QA2PJTM4","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1602.08557&json=true","fetch_graph":"https://pith.science/api/pith-number/QA2PJTM42GWGGSDFA7WZAFGHMD/graph.json","fetch_events":"https://pith.science/api/pith-number/QA2PJTM42GWGGSDFA7WZAFGHMD/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/QA2PJTM42GWGGSDFA7WZAFGHMD/action/timestamp_anchor","attest_storage":"https://pith.science/pith/QA2PJTM42GWGGSDFA7WZAFGHMD/action/storage_attestation","attest_author":"https://pith.science/pith/QA2PJTM42GWGGSDFA7WZAFGHMD/action/author_attestation","sign_citation":"https://pith.science/pith/QA2PJTM42GWGGSDFA7WZAFGHMD/action/citation_signature","submit_replication":"https://pith.science/pith/QA2PJTM42GWGGSDFA7WZAFGHMD/action/replication_record"}},"created_at":"2026-05-18T00:30:55.507487+00:00","updated_at":"2026-05-18T00:30:55.507487+00:00"}