{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:2LSHX57B5XAVY7L7Z35VI7N643","short_pith_number":"pith:2LSHX57B","schema_version":"1.0","canonical_sha256":"d2e47bf7e1edc15c7d7fcefb547dbee6c62f43931b5197ad3ecd54a0e503a0cd","source":{"kind":"arxiv","id":"1903.11194","version":2},"attestation_state":"computed","paper":{"title":"Harnessing Intrinsic Noise in Memristor Hopfield Neural Networks for Combinatorial Optimization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.ET","authors_text":"Can Li, Fuxi Cai, J. Joshua Yang, John Paul Strachan, Qiangfei Xia, Raymond Beausoleil, Rui Liu, Shimeng Yu, Suhas Kumar, Thomas Van Vaerenbergh, Wei Lu","submitted_at":"2019-03-26T23:57:10Z","abstract_excerpt":"We describe a hybrid analog-digital computing approach to solve important combinatorial optimization problems that leverages memristors (two-terminal nonvolatile memories). While previous memristor accelerators have had to minimize analog noise effects, we show that our optimization solver harnesses such noise as a computing resource. Here we describe a memristor-Hopfield Neural Network (mem-HNN) with massively parallel operations performed in a dense crossbar array. We provide experimental demonstrations solving NP-hard max-cut problems directly in analog crossbar arrays, and supplement this "},"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":"1903.11194","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.ET","submitted_at":"2019-03-26T23:57:10Z","cross_cats_sorted":[],"title_canon_sha256":"ea9de398b4f331bac800e0366b9ae0b11bffd57fc7aa9dc5df1d54c04e55e0e5","abstract_canon_sha256":"0bf9b4fb12292608c7538bc97aa6192335237db8622ba3a62a62359b0d39b96c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:49:24.426758Z","signature_b64":"zdgmLd6e4aJbpf0K/I2469lfYMudbXeIT05hgbkcIxmCL842CDZcq4ybuGVr3Ha/CGTrOQx+D2AwTrQfX0krAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d2e47bf7e1edc15c7d7fcefb547dbee6c62f43931b5197ad3ecd54a0e503a0cd","last_reissued_at":"2026-05-17T23:49:24.426203Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:49:24.426203Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Harnessing Intrinsic Noise in Memristor Hopfield Neural Networks for Combinatorial Optimization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.ET","authors_text":"Can Li, Fuxi Cai, J. Joshua Yang, John Paul Strachan, Qiangfei Xia, Raymond Beausoleil, Rui Liu, Shimeng Yu, Suhas Kumar, Thomas Van Vaerenbergh, Wei Lu","submitted_at":"2019-03-26T23:57:10Z","abstract_excerpt":"We describe a hybrid analog-digital computing approach to solve important combinatorial optimization problems that leverages memristors (two-terminal nonvolatile memories). While previous memristor accelerators have had to minimize analog noise effects, we show that our optimization solver harnesses such noise as a computing resource. Here we describe a memristor-Hopfield Neural Network (mem-HNN) with massively parallel operations performed in a dense crossbar array. We provide experimental demonstrations solving NP-hard max-cut problems directly in analog crossbar arrays, and supplement this "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1903.11194","kind":"arxiv","version":2},"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":"1903.11194","created_at":"2026-05-17T23:49:24.426283+00:00"},{"alias_kind":"arxiv_version","alias_value":"1903.11194v2","created_at":"2026-05-17T23:49:24.426283+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1903.11194","created_at":"2026-05-17T23:49:24.426283+00:00"},{"alias_kind":"pith_short_12","alias_value":"2LSHX57B5XAV","created_at":"2026-05-18T12:33:07.085635+00:00"},{"alias_kind":"pith_short_16","alias_value":"2LSHX57B5XAVY7L7","created_at":"2026-05-18T12:33:07.085635+00:00"},{"alias_kind":"pith_short_8","alias_value":"2LSHX57B","created_at":"2026-05-18T12:33:07.085635+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/2LSHX57B5XAVY7L7Z35VI7N643","json":"https://pith.science/pith/2LSHX57B5XAVY7L7Z35VI7N643.json","graph_json":"https://pith.science/api/pith-number/2LSHX57B5XAVY7L7Z35VI7N643/graph.json","events_json":"https://pith.science/api/pith-number/2LSHX57B5XAVY7L7Z35VI7N643/events.json","paper":"https://pith.science/paper/2LSHX57B"},"agent_actions":{"view_html":"https://pith.science/pith/2LSHX57B5XAVY7L7Z35VI7N643","download_json":"https://pith.science/pith/2LSHX57B5XAVY7L7Z35VI7N643.json","view_paper":"https://pith.science/paper/2LSHX57B","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1903.11194&json=true","fetch_graph":"https://pith.science/api/pith-number/2LSHX57B5XAVY7L7Z35VI7N643/graph.json","fetch_events":"https://pith.science/api/pith-number/2LSHX57B5XAVY7L7Z35VI7N643/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/2LSHX57B5XAVY7L7Z35VI7N643/action/timestamp_anchor","attest_storage":"https://pith.science/pith/2LSHX57B5XAVY7L7Z35VI7N643/action/storage_attestation","attest_author":"https://pith.science/pith/2LSHX57B5XAVY7L7Z35VI7N643/action/author_attestation","sign_citation":"https://pith.science/pith/2LSHX57B5XAVY7L7Z35VI7N643/action/citation_signature","submit_replication":"https://pith.science/pith/2LSHX57B5XAVY7L7Z35VI7N643/action/replication_record"}},"created_at":"2026-05-17T23:49:24.426283+00:00","updated_at":"2026-05-17T23:49:24.426283+00:00"}