{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:56ZM7KJFXMM3SZ7FZIE4J3MSPV","short_pith_number":"pith:56ZM7KJF","schema_version":"1.0","canonical_sha256":"efb2cfa925bb19b967e5ca09c4ed927d640a17eae19d7fba186fbea47f5df33b","source":{"kind":"arxiv","id":"1803.04773","version":1},"attestation_state":"computed","paper":{"title":"A case for multiple and parallel RRAMs as synaptic model for training SNNs","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.NE"],"primary_cat":"cs.ET","authors_text":"Aditya Shukla, Sandip Lashkare, Sidharth Prasad, Udayan Ganguly","submitted_at":"2018-03-13T13:13:59Z","abstract_excerpt":"To enable a dense integration of model synapses in a spiking neural networks hardware, various nano-scale devices are being considered. Such a device, besides exhibiting spike-time dependent plasticity (STDP), needs to be highly scalable, have a large endurance and require low energy for transitioning between states. In this work, we first introduce and empirically determine two new specifications for an synapse in SNNs: number of conductance levels per synapse and maximum learning-rate. To the best of our knowledge, there are no RRAMs that meet the latter specification. As a solution, we prop"},"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":"1803.04773","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.ET","submitted_at":"2018-03-13T13:13:59Z","cross_cats_sorted":["cs.NE"],"title_canon_sha256":"bbf6e69dae719b739e6ddaf9c312f32c25d8f77273c48664b09268f2410a7819","abstract_canon_sha256":"c3dabae265c44307b7fb217e7228a61f4eba9bfd7cef9d57303a74a2595ec6f9"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:21:17.926345Z","signature_b64":"0+XhlM2C4AppGbxgfXO/3siitoBQHQbyL4jwadv74Zn8KRFENwGZEN4QCxXgJ0zdZofM2L7OOaTpjI4c6wtTDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"efb2cfa925bb19b967e5ca09c4ed927d640a17eae19d7fba186fbea47f5df33b","last_reissued_at":"2026-05-18T00:21:17.925854Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:21:17.925854Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A case for multiple and parallel RRAMs as synaptic model for training SNNs","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.NE"],"primary_cat":"cs.ET","authors_text":"Aditya Shukla, Sandip Lashkare, Sidharth Prasad, Udayan Ganguly","submitted_at":"2018-03-13T13:13:59Z","abstract_excerpt":"To enable a dense integration of model synapses in a spiking neural networks hardware, various nano-scale devices are being considered. Such a device, besides exhibiting spike-time dependent plasticity (STDP), needs to be highly scalable, have a large endurance and require low energy for transitioning between states. In this work, we first introduce and empirically determine two new specifications for an synapse in SNNs: number of conductance levels per synapse and maximum learning-rate. To the best of our knowledge, there are no RRAMs that meet the latter specification. As a solution, we prop"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1803.04773","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":""},"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":"1803.04773","created_at":"2026-05-18T00:21:17.925936+00:00"},{"alias_kind":"arxiv_version","alias_value":"1803.04773v1","created_at":"2026-05-18T00:21:17.925936+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1803.04773","created_at":"2026-05-18T00:21:17.925936+00:00"},{"alias_kind":"pith_short_12","alias_value":"56ZM7KJFXMM3","created_at":"2026-05-18T12:32:08.215937+00:00"},{"alias_kind":"pith_short_16","alias_value":"56ZM7KJFXMM3SZ7F","created_at":"2026-05-18T12:32:08.215937+00:00"},{"alias_kind":"pith_short_8","alias_value":"56ZM7KJF","created_at":"2026-05-18T12:32:08.215937+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/56ZM7KJFXMM3SZ7FZIE4J3MSPV","json":"https://pith.science/pith/56ZM7KJFXMM3SZ7FZIE4J3MSPV.json","graph_json":"https://pith.science/api/pith-number/56ZM7KJFXMM3SZ7FZIE4J3MSPV/graph.json","events_json":"https://pith.science/api/pith-number/56ZM7KJFXMM3SZ7FZIE4J3MSPV/events.json","paper":"https://pith.science/paper/56ZM7KJF"},"agent_actions":{"view_html":"https://pith.science/pith/56ZM7KJFXMM3SZ7FZIE4J3MSPV","download_json":"https://pith.science/pith/56ZM7KJFXMM3SZ7FZIE4J3MSPV.json","view_paper":"https://pith.science/paper/56ZM7KJF","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1803.04773&json=true","fetch_graph":"https://pith.science/api/pith-number/56ZM7KJFXMM3SZ7FZIE4J3MSPV/graph.json","fetch_events":"https://pith.science/api/pith-number/56ZM7KJFXMM3SZ7FZIE4J3MSPV/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/56ZM7KJFXMM3SZ7FZIE4J3MSPV/action/timestamp_anchor","attest_storage":"https://pith.science/pith/56ZM7KJFXMM3SZ7FZIE4J3MSPV/action/storage_attestation","attest_author":"https://pith.science/pith/56ZM7KJFXMM3SZ7FZIE4J3MSPV/action/author_attestation","sign_citation":"https://pith.science/pith/56ZM7KJFXMM3SZ7FZIE4J3MSPV/action/citation_signature","submit_replication":"https://pith.science/pith/56ZM7KJFXMM3SZ7FZIE4J3MSPV/action/replication_record"}},"created_at":"2026-05-18T00:21:17.925936+00:00","updated_at":"2026-05-18T00:21:17.925936+00:00"}