{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:5FWWQB7S7COWZ2YHSP3H3VHU52","short_pith_number":"pith:5FWWQB7S","schema_version":"1.0","canonical_sha256":"e96d6807f2f89d6ceb0793f67dd4f4eeb74e025407b63ccf1b7b371dd14c45d1","source":{"kind":"arxiv","id":"2505.14303","version":3},"attestation_state":"computed","paper":{"title":"Optimizing Binary and Ternary Neural Network Inference on RRAM Crossbars using CIM-Explorer","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.ET","authors_text":"Florian Idrizi, Jan Moritz Joseph, Jos\\'e Cubero-Cascante, Lennart M. Reimann, Niklas Degener, Nils Bosbach, Rainer Leupers, Rebecca Pelke","submitted_at":"2025-05-20T12:54:48Z","abstract_excerpt":"Using Resistive Random Access Memory (RRAM) crossbars in Computing-in-Memory (CIM) architectures offers a promising solution to overcome the von Neumann bottleneck. Due to non-idealities like cell variability, RRAM crossbars are often operated in binary mode, utilizing only two states: Low Resistive State (LRS) and High Resistive State (HRS). Binary Neural Networks (BNNs) and Ternary Neural Networks (TNNs) are well-suited for this hardware due to their efficient mapping. Existing software projects for RRAM-based CIM typically focus on only one aspect: compilation, simulation, or Design Space E"},"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":"2505.14303","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.ET","submitted_at":"2025-05-20T12:54:48Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"32d7e6d1414c8b4537f3d969c2408b26f83e3eca9161d58497a10701d4f2b219","abstract_canon_sha256":"6cc805b19d95b90d8d51b8dbca73762db319fc76dbecd04457b0b9b4a132911d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-16T01:22:25.427118Z","signature_b64":"JR9MH3/XmZJY83Px736Gtho0u46TDsurN1p9588vNvUJP0eL0EXfiQV4QKQt7DGloedhw8goulnyTP+c8JFBDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e96d6807f2f89d6ceb0793f67dd4f4eeb74e025407b63ccf1b7b371dd14c45d1","last_reissued_at":"2026-07-16T01:22:25.426128Z","signature_status":"signed_v1","first_computed_at":"2026-07-16T01:22:25.426128Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Optimizing Binary and Ternary Neural Network Inference on RRAM Crossbars using CIM-Explorer","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.ET","authors_text":"Florian Idrizi, Jan Moritz Joseph, Jos\\'e Cubero-Cascante, Lennart M. Reimann, Niklas Degener, Nils Bosbach, Rainer Leupers, Rebecca Pelke","submitted_at":"2025-05-20T12:54:48Z","abstract_excerpt":"Using Resistive Random Access Memory (RRAM) crossbars in Computing-in-Memory (CIM) architectures offers a promising solution to overcome the von Neumann bottleneck. Due to non-idealities like cell variability, RRAM crossbars are often operated in binary mode, utilizing only two states: Low Resistive State (LRS) and High Resistive State (HRS). Binary Neural Networks (BNNs) and Ternary Neural Networks (TNNs) are well-suited for this hardware due to their efficient mapping. Existing software projects for RRAM-based CIM typically focus on only one aspect: compilation, simulation, or Design Space E"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.14303","kind":"arxiv","version":3},"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/2505.14303/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":"2505.14303","created_at":"2026-07-16T01:22:25.426596+00:00"},{"alias_kind":"arxiv_version","alias_value":"2505.14303v3","created_at":"2026-07-16T01:22:25.426596+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.14303","created_at":"2026-07-16T01:22:25.426596+00:00"},{"alias_kind":"pith_short_12","alias_value":"5FWWQB7S7COW","created_at":"2026-07-16T01:22:25.426596+00:00"},{"alias_kind":"pith_short_16","alias_value":"5FWWQB7S7COWZ2YH","created_at":"2026-07-16T01:22:25.426596+00:00"},{"alias_kind":"pith_short_8","alias_value":"5FWWQB7S","created_at":"2026-07-16T01:22:25.426596+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/5FWWQB7S7COWZ2YHSP3H3VHU52","json":"https://pith.science/pith/5FWWQB7S7COWZ2YHSP3H3VHU52.json","graph_json":"https://pith.science/api/pith-number/5FWWQB7S7COWZ2YHSP3H3VHU52/graph.json","events_json":"https://pith.science/api/pith-number/5FWWQB7S7COWZ2YHSP3H3VHU52/events.json","paper":"https://pith.science/paper/5FWWQB7S"},"agent_actions":{"view_html":"https://pith.science/pith/5FWWQB7S7COWZ2YHSP3H3VHU52","download_json":"https://pith.science/pith/5FWWQB7S7COWZ2YHSP3H3VHU52.json","view_paper":"https://pith.science/paper/5FWWQB7S","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2505.14303&json=true","fetch_graph":"https://pith.science/api/pith-number/5FWWQB7S7COWZ2YHSP3H3VHU52/graph.json","fetch_events":"https://pith.science/api/pith-number/5FWWQB7S7COWZ2YHSP3H3VHU52/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/5FWWQB7S7COWZ2YHSP3H3VHU52/action/timestamp_anchor","attest_storage":"https://pith.science/pith/5FWWQB7S7COWZ2YHSP3H3VHU52/action/storage_attestation","attest_author":"https://pith.science/pith/5FWWQB7S7COWZ2YHSP3H3VHU52/action/author_attestation","sign_citation":"https://pith.science/pith/5FWWQB7S7COWZ2YHSP3H3VHU52/action/citation_signature","submit_replication":"https://pith.science/pith/5FWWQB7S7COWZ2YHSP3H3VHU52/action/replication_record"}},"created_at":"2026-07-16T01:22:25.426596+00:00","updated_at":"2026-07-16T01:22:25.426596+00:00"}