{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:WSXZTGT45B3GJESUXI5HRXRRLH","short_pith_number":"pith:WSXZTGT4","schema_version":"1.0","canonical_sha256":"b4af999a7ce876649254ba3a78de3159f70c46d87bc19c9e733ee0f4b03fce51","source":{"kind":"arxiv","id":"2112.10547","version":1},"attestation_state":"computed","paper":{"title":"A Memristor-Based Bayesian Machine","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.ET","authors_text":"Cl\\'ement Turck, Damien Querlioz, Elisa Vianello, Jacques Droulez, Jean-Michel Portal, Kamel-Eddine Harabi, Marc Bocquet, Pierre Bessi\\`ere, Rapha\\\"el Laurent, Tifenn Hirtzlin","submitted_at":"2021-12-20T14:16:11Z","abstract_excerpt":"In recent years, a considerable research effort has shown the energy benefits of implementing neural networks with memristors or other emerging memory technologies. However, for extreme-edge applications with high uncertainty, access to reduced amounts of data, and where explainable decisions are required, neural networks may not provide an acceptable form of intelligence. Bayesian reasoning can solve these concerns, but it is computationally expensive and, unlike neural networks, does not translate naturally to memristor-based architectures. In this work, we introduce, demonstrate experimenta"},"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":"2112.10547","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.ET","submitted_at":"2021-12-20T14:16:11Z","cross_cats_sorted":[],"title_canon_sha256":"514d8cfcde5ee56f0a1d4df8494eef43df14192ec5893e33f96c9bceddb80696","abstract_canon_sha256":"a077c4c8d787534875a3d2a0dd7a7b27c09e58c16073d2937e3382b4053fbd98"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:42:16.482709Z","signature_b64":"40c9Vh1PLjn3+By14K7Rqeilk8aMWVngP6J97THnsVs6ANE6gcwBNHXIB4niVAIKyUKVSDwuHtEClMY4Aji0Cw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b4af999a7ce876649254ba3a78de3159f70c46d87bc19c9e733ee0f4b03fce51","last_reissued_at":"2026-07-05T03:42:16.482343Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:42:16.482343Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Memristor-Based Bayesian Machine","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.ET","authors_text":"Cl\\'ement Turck, Damien Querlioz, Elisa Vianello, Jacques Droulez, Jean-Michel Portal, Kamel-Eddine Harabi, Marc Bocquet, Pierre Bessi\\`ere, Rapha\\\"el Laurent, Tifenn Hirtzlin","submitted_at":"2021-12-20T14:16:11Z","abstract_excerpt":"In recent years, a considerable research effort has shown the energy benefits of implementing neural networks with memristors or other emerging memory technologies. However, for extreme-edge applications with high uncertainty, access to reduced amounts of data, and where explainable decisions are required, neural networks may not provide an acceptable form of intelligence. Bayesian reasoning can solve these concerns, but it is computationally expensive and, unlike neural networks, does not translate naturally to memristor-based architectures. In this work, we introduce, demonstrate experimenta"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2112.10547","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2112.10547/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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":"2112.10547","created_at":"2026-07-05T03:42:16.482399+00:00"},{"alias_kind":"arxiv_version","alias_value":"2112.10547v1","created_at":"2026-07-05T03:42:16.482399+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2112.10547","created_at":"2026-07-05T03:42:16.482399+00:00"},{"alias_kind":"pith_short_12","alias_value":"WSXZTGT45B3G","created_at":"2026-07-05T03:42:16.482399+00:00"},{"alias_kind":"pith_short_16","alias_value":"WSXZTGT45B3GJESU","created_at":"2026-07-05T03:42:16.482399+00:00"},{"alias_kind":"pith_short_8","alias_value":"WSXZTGT4","created_at":"2026-07-05T03:42:16.482399+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/WSXZTGT45B3GJESUXI5HRXRRLH","json":"https://pith.science/pith/WSXZTGT45B3GJESUXI5HRXRRLH.json","graph_json":"https://pith.science/api/pith-number/WSXZTGT45B3GJESUXI5HRXRRLH/graph.json","events_json":"https://pith.science/api/pith-number/WSXZTGT45B3GJESUXI5HRXRRLH/events.json","paper":"https://pith.science/paper/WSXZTGT4"},"agent_actions":{"view_html":"https://pith.science/pith/WSXZTGT45B3GJESUXI5HRXRRLH","download_json":"https://pith.science/pith/WSXZTGT45B3GJESUXI5HRXRRLH.json","view_paper":"https://pith.science/paper/WSXZTGT4","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2112.10547&json=true","fetch_graph":"https://pith.science/api/pith-number/WSXZTGT45B3GJESUXI5HRXRRLH/graph.json","fetch_events":"https://pith.science/api/pith-number/WSXZTGT45B3GJESUXI5HRXRRLH/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/WSXZTGT45B3GJESUXI5HRXRRLH/action/timestamp_anchor","attest_storage":"https://pith.science/pith/WSXZTGT45B3GJESUXI5HRXRRLH/action/storage_attestation","attest_author":"https://pith.science/pith/WSXZTGT45B3GJESUXI5HRXRRLH/action/author_attestation","sign_citation":"https://pith.science/pith/WSXZTGT45B3GJESUXI5HRXRRLH/action/citation_signature","submit_replication":"https://pith.science/pith/WSXZTGT45B3GJESUXI5HRXRRLH/action/replication_record"}},"created_at":"2026-07-05T03:42:16.482399+00:00","updated_at":"2026-07-05T03:42:16.482399+00:00"}