{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:TMNB2MCR44CIRXVRKIVI2U3AE5","short_pith_number":"pith:TMNB2MCR","schema_version":"1.0","canonical_sha256":"9b1a1d3051e70488deb1522a8d5360277ab9a9fc282046434e2d8713332a9ed8","source":{"kind":"arxiv","id":"2606.02781","version":1},"attestation_state":"computed","paper":{"title":"CRAM-ER: Error-Resilient Spintronic Computational Random Access Memory for Scalable In-Memory Computation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.ET"],"primary_cat":"cs.AR","authors_text":"Brahmdutta Dixit, Cheng Wang, Jian-Ping Wang, Md. Shahedul Hasan, Sohan Salahuddin Mugdho, Yang Lv","submitted_at":"2026-06-01T18:45:05Z","abstract_excerpt":"Deep neural networks (DNNs) have achieved state-of-the-art performance across diverse domains. However, typical Von Neumann compute paradigms face severe memory bottlenecks. Emerging near-memory and compute-in-memory approaches alleviate this but incur significant peripheral overhead. Computational Random Access Memory (CRAM) based on MRAM enables in-situ logic without peripheral overhead, offering a dense, energy-efficient solution. However, probabilistic MRAM switching induces gate-level errors that limit the scalability and reliability of CRAM for accelerating DNN. Moreover, the large numbe"},"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":"2606.02781","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AR","submitted_at":"2026-06-01T18:45:05Z","cross_cats_sorted":["cs.AI","cs.ET"],"title_canon_sha256":"83afdbb519f2f67ebb3066bb26778c00dddd9455f436f4eb82c5ff8761a7acea","abstract_canon_sha256":"2928c5c748832f60af6fab552a5dc923d8509c202cf7c0312a6d0a4c00d9ecca"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-03T01:05:21.588083Z","signature_b64":"EX6GLSJOW8doYTbIRcSBm2y/Cye2sk/Q/sXuayBYTwIZizfzp8GPKFmcsiHrLUylMXIzT+QMWkDwHQHj/LGyBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9b1a1d3051e70488deb1522a8d5360277ab9a9fc282046434e2d8713332a9ed8","last_reissued_at":"2026-06-03T01:05:21.587648Z","signature_status":"signed_v1","first_computed_at":"2026-06-03T01:05:21.587648Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"CRAM-ER: Error-Resilient Spintronic Computational Random Access Memory for Scalable In-Memory Computation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.ET"],"primary_cat":"cs.AR","authors_text":"Brahmdutta Dixit, Cheng Wang, Jian-Ping Wang, Md. Shahedul Hasan, Sohan Salahuddin Mugdho, Yang Lv","submitted_at":"2026-06-01T18:45:05Z","abstract_excerpt":"Deep neural networks (DNNs) have achieved state-of-the-art performance across diverse domains. However, typical Von Neumann compute paradigms face severe memory bottlenecks. Emerging near-memory and compute-in-memory approaches alleviate this but incur significant peripheral overhead. Computational Random Access Memory (CRAM) based on MRAM enables in-situ logic without peripheral overhead, offering a dense, energy-efficient solution. However, probabilistic MRAM switching induces gate-level errors that limit the scalability and reliability of CRAM for accelerating DNN. Moreover, the large numbe"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.02781","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/2606.02781/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":"2606.02781","created_at":"2026-06-03T01:05:21.587704+00:00"},{"alias_kind":"arxiv_version","alias_value":"2606.02781v1","created_at":"2026-06-03T01:05:21.587704+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.02781","created_at":"2026-06-03T01:05:21.587704+00:00"},{"alias_kind":"pith_short_12","alias_value":"TMNB2MCR44CI","created_at":"2026-06-03T01:05:21.587704+00:00"},{"alias_kind":"pith_short_16","alias_value":"TMNB2MCR44CIRXVR","created_at":"2026-06-03T01:05:21.587704+00:00"},{"alias_kind":"pith_short_8","alias_value":"TMNB2MCR","created_at":"2026-06-03T01:05:21.587704+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/TMNB2MCR44CIRXVRKIVI2U3AE5","json":"https://pith.science/pith/TMNB2MCR44CIRXVRKIVI2U3AE5.json","graph_json":"https://pith.science/api/pith-number/TMNB2MCR44CIRXVRKIVI2U3AE5/graph.json","events_json":"https://pith.science/api/pith-number/TMNB2MCR44CIRXVRKIVI2U3AE5/events.json","paper":"https://pith.science/paper/TMNB2MCR"},"agent_actions":{"view_html":"https://pith.science/pith/TMNB2MCR44CIRXVRKIVI2U3AE5","download_json":"https://pith.science/pith/TMNB2MCR44CIRXVRKIVI2U3AE5.json","view_paper":"https://pith.science/paper/TMNB2MCR","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2606.02781&json=true","fetch_graph":"https://pith.science/api/pith-number/TMNB2MCR44CIRXVRKIVI2U3AE5/graph.json","fetch_events":"https://pith.science/api/pith-number/TMNB2MCR44CIRXVRKIVI2U3AE5/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/TMNB2MCR44CIRXVRKIVI2U3AE5/action/timestamp_anchor","attest_storage":"https://pith.science/pith/TMNB2MCR44CIRXVRKIVI2U3AE5/action/storage_attestation","attest_author":"https://pith.science/pith/TMNB2MCR44CIRXVRKIVI2U3AE5/action/author_attestation","sign_citation":"https://pith.science/pith/TMNB2MCR44CIRXVRKIVI2U3AE5/action/citation_signature","submit_replication":"https://pith.science/pith/TMNB2MCR44CIRXVRKIVI2U3AE5/action/replication_record"}},"created_at":"2026-06-03T01:05:21.587704+00:00","updated_at":"2026-06-03T01:05:21.587704+00:00"}