{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2016:D6IPRVS2OSXLNMTOARYGARZJL2","short_pith_number":"pith:D6IPRVS2","schema_version":"1.0","canonical_sha256":"1f90f8d65a74aeb6b26e04706047295ea4c3853007d3407b2502958d65570683","source":{"kind":"arxiv","id":"1603.07341","version":1},"attestation_state":"computed","paper":{"title":"Acceleration of Deep Neural Network Training with Resistive Cross-Point Devices","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.NE","stat.ML"],"primary_cat":"cs.LG","authors_text":"Tayfun Gokmen, Yurii Vlasov","submitted_at":"2016-03-23T20:13:11Z","abstract_excerpt":"In recent years, deep neural networks (DNN) have demonstrated significant business impact in large scale analysis and classification tasks such as speech recognition, visual object detection, pattern extraction, etc. Training of large DNNs, however, is universally considered as time consuming and computationally intensive task that demands datacenter-scale computational resources recruited for many days. Here we propose a concept of resistive processing unit (RPU) devices that can potentially accelerate DNN training by orders of magnitude while using much less power. The proposed RPU device ca"},"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":"1603.07341","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2016-03-23T20:13:11Z","cross_cats_sorted":["cs.NE","stat.ML"],"title_canon_sha256":"40782b9bc11068ea219a3345d6d91ba1e8807ecf9355eb41cb10d4ef87ad488b","abstract_canon_sha256":"dc87c7d55804aa8e3352a4f7fa48067ac2a59121d38a440028026aa3d17ac0a2"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:44:01.357040Z","signature_b64":"Q4TS0Eq/LqzA41VDks/uaJ9RdQnHOYc9Ic/dn5Pt5aClMmseDeCUuNsEx5wWq6iNHlVb06aJ0TL5G8MW5W3pCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1f90f8d65a74aeb6b26e04706047295ea4c3853007d3407b2502958d65570683","last_reissued_at":"2026-05-18T00:44:01.356352Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:44:01.356352Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Acceleration of Deep Neural Network Training with Resistive Cross-Point Devices","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.NE","stat.ML"],"primary_cat":"cs.LG","authors_text":"Tayfun Gokmen, Yurii Vlasov","submitted_at":"2016-03-23T20:13:11Z","abstract_excerpt":"In recent years, deep neural networks (DNN) have demonstrated significant business impact in large scale analysis and classification tasks such as speech recognition, visual object detection, pattern extraction, etc. Training of large DNNs, however, is universally considered as time consuming and computationally intensive task that demands datacenter-scale computational resources recruited for many days. Here we propose a concept of resistive processing unit (RPU) devices that can potentially accelerate DNN training by orders of magnitude while using much less power. The proposed RPU device ca"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1603.07341","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":"1603.07341","created_at":"2026-05-18T00:44:01.356458+00:00"},{"alias_kind":"arxiv_version","alias_value":"1603.07341v1","created_at":"2026-05-18T00:44:01.356458+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1603.07341","created_at":"2026-05-18T00:44:01.356458+00:00"},{"alias_kind":"pith_short_12","alias_value":"D6IPRVS2OSXL","created_at":"2026-05-18T12:30:09.641336+00:00"},{"alias_kind":"pith_short_16","alias_value":"D6IPRVS2OSXLNMTO","created_at":"2026-05-18T12:30:09.641336+00:00"},{"alias_kind":"pith_short_8","alias_value":"D6IPRVS2","created_at":"2026-05-18T12:30:09.641336+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/D6IPRVS2OSXLNMTOARYGARZJL2","json":"https://pith.science/pith/D6IPRVS2OSXLNMTOARYGARZJL2.json","graph_json":"https://pith.science/api/pith-number/D6IPRVS2OSXLNMTOARYGARZJL2/graph.json","events_json":"https://pith.science/api/pith-number/D6IPRVS2OSXLNMTOARYGARZJL2/events.json","paper":"https://pith.science/paper/D6IPRVS2"},"agent_actions":{"view_html":"https://pith.science/pith/D6IPRVS2OSXLNMTOARYGARZJL2","download_json":"https://pith.science/pith/D6IPRVS2OSXLNMTOARYGARZJL2.json","view_paper":"https://pith.science/paper/D6IPRVS2","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1603.07341&json=true","fetch_graph":"https://pith.science/api/pith-number/D6IPRVS2OSXLNMTOARYGARZJL2/graph.json","fetch_events":"https://pith.science/api/pith-number/D6IPRVS2OSXLNMTOARYGARZJL2/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/D6IPRVS2OSXLNMTOARYGARZJL2/action/timestamp_anchor","attest_storage":"https://pith.science/pith/D6IPRVS2OSXLNMTOARYGARZJL2/action/storage_attestation","attest_author":"https://pith.science/pith/D6IPRVS2OSXLNMTOARYGARZJL2/action/author_attestation","sign_citation":"https://pith.science/pith/D6IPRVS2OSXLNMTOARYGARZJL2/action/citation_signature","submit_replication":"https://pith.science/pith/D6IPRVS2OSXLNMTOARYGARZJL2/action/replication_record"}},"created_at":"2026-05-18T00:44:01.356458+00:00","updated_at":"2026-05-18T00:44:01.356458+00:00"}