{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:VK7JRMOK3ULAN4ZAW4LBFZB4MK","short_pith_number":"pith:VK7JRMOK","schema_version":"1.0","canonical_sha256":"aabe98b1cadd1606f320b71612e43c62b4569279609d6fe8a85d2a9ba8ff6929","source":{"kind":"arxiv","id":"2304.11337","version":1},"attestation_state":"computed","paper":{"title":"A Deep Neural Network Deployment Based on Resistive Memory Accelerator Simulation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.NE"],"primary_cat":"cs.AR","authors_text":"Bindu B, Ria Barnwal, Tejaswanth Reddy Maram","submitted_at":"2023-04-22T07:29:02Z","abstract_excerpt":"The objective of this study is to illustrate the process of training a Deep Neural Network (DNN) within a Resistive RAM (ReRAM) Crossbar-based simulation environment using CrossSim, an Application Programming Interface (API) developed for this purpose. The CrossSim API is designed to simulate neural networks while taking into account factors that may affect the accuracy of solutions during training on non-linear and noisy ReRAM devices. ReRAM-based neural cores that serve as memory accelerators for digital cores on a chip can significantly reduce energy consumption by minimizing data transfers"},"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":"2304.11337","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AR","submitted_at":"2023-04-22T07:29:02Z","cross_cats_sorted":["cs.NE"],"title_canon_sha256":"e9dd98108264ca3bd471785a1ac3721b607397fa5de5886148aa4cf104c3eb1c","abstract_canon_sha256":"98982fce6c030f4652a388119b091e0f71b206595e25babfa5922d9b1c9344ba"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:00:50.519095Z","signature_b64":"re6Dwzu0ccVvPTvs9DyUaCphqQ+CrqFlog0G9vB+1TVzK3H9hoD5V2slwS5PHKBxV92L04+be3SwDg40c9ltBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"aabe98b1cadd1606f320b71612e43c62b4569279609d6fe8a85d2a9ba8ff6929","last_reissued_at":"2026-07-05T09:00:50.518600Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:00:50.518600Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Deep Neural Network Deployment Based on Resistive Memory Accelerator Simulation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.NE"],"primary_cat":"cs.AR","authors_text":"Bindu B, Ria Barnwal, Tejaswanth Reddy Maram","submitted_at":"2023-04-22T07:29:02Z","abstract_excerpt":"The objective of this study is to illustrate the process of training a Deep Neural Network (DNN) within a Resistive RAM (ReRAM) Crossbar-based simulation environment using CrossSim, an Application Programming Interface (API) developed for this purpose. The CrossSim API is designed to simulate neural networks while taking into account factors that may affect the accuracy of solutions during training on non-linear and noisy ReRAM devices. ReRAM-based neural cores that serve as memory accelerators for digital cores on a chip can significantly reduce energy consumption by minimizing data transfers"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2304.11337","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/2304.11337/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":"2304.11337","created_at":"2026-07-05T09:00:50.518663+00:00"},{"alias_kind":"arxiv_version","alias_value":"2304.11337v1","created_at":"2026-07-05T09:00:50.518663+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2304.11337","created_at":"2026-07-05T09:00:50.518663+00:00"},{"alias_kind":"pith_short_12","alias_value":"VK7JRMOK3ULA","created_at":"2026-07-05T09:00:50.518663+00:00"},{"alias_kind":"pith_short_16","alias_value":"VK7JRMOK3ULAN4ZA","created_at":"2026-07-05T09:00:50.518663+00:00"},{"alias_kind":"pith_short_8","alias_value":"VK7JRMOK","created_at":"2026-07-05T09:00:50.518663+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2506.01387","citing_title":"Multi Part Deployment of Neural Network","ref_index":7,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/VK7JRMOK3ULAN4ZAW4LBFZB4MK","json":"https://pith.science/pith/VK7JRMOK3ULAN4ZAW4LBFZB4MK.json","graph_json":"https://pith.science/api/pith-number/VK7JRMOK3ULAN4ZAW4LBFZB4MK/graph.json","events_json":"https://pith.science/api/pith-number/VK7JRMOK3ULAN4ZAW4LBFZB4MK/events.json","paper":"https://pith.science/paper/VK7JRMOK"},"agent_actions":{"view_html":"https://pith.science/pith/VK7JRMOK3ULAN4ZAW4LBFZB4MK","download_json":"https://pith.science/pith/VK7JRMOK3ULAN4ZAW4LBFZB4MK.json","view_paper":"https://pith.science/paper/VK7JRMOK","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2304.11337&json=true","fetch_graph":"https://pith.science/api/pith-number/VK7JRMOK3ULAN4ZAW4LBFZB4MK/graph.json","fetch_events":"https://pith.science/api/pith-number/VK7JRMOK3ULAN4ZAW4LBFZB4MK/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/VK7JRMOK3ULAN4ZAW4LBFZB4MK/action/timestamp_anchor","attest_storage":"https://pith.science/pith/VK7JRMOK3ULAN4ZAW4LBFZB4MK/action/storage_attestation","attest_author":"https://pith.science/pith/VK7JRMOK3ULAN4ZAW4LBFZB4MK/action/author_attestation","sign_citation":"https://pith.science/pith/VK7JRMOK3ULAN4ZAW4LBFZB4MK/action/citation_signature","submit_replication":"https://pith.science/pith/VK7JRMOK3ULAN4ZAW4LBFZB4MK/action/replication_record"}},"created_at":"2026-07-05T09:00:50.518663+00:00","updated_at":"2026-07-05T09:00:50.518663+00:00"}