{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:RFYQDXZH5A7YKZA2FZ3RBNYGLV","short_pith_number":"pith:RFYQDXZH","schema_version":"1.0","canonical_sha256":"897101df27e83f85641a2e7710b7065d5ec7424d283f50107be99273b6ad95d8","source":{"kind":"arxiv","id":"2210.12899","version":1},"attestation_state":"computed","paper":{"title":"SpikeSim: An end-to-end Compute-in-Memory Hardware Evaluation Tool for Benchmarking Spiking Neural Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"cs.NE","authors_text":"Abhiroop Bhattacharjee, Abhishek Moitra, Gokul Krishnan, Priyadarshini Panda, Runcong Kuang, Yu Cao","submitted_at":"2022-10-24T01:07:17Z","abstract_excerpt":"SNNs are an active research domain towards energy efficient machine intelligence. Compared to conventional ANNs, SNNs use temporal spike data and bio-plausible neuronal activation functions such as Leaky-Integrate Fire/Integrate Fire (LIF/IF) for data processing. However, SNNs incur significant dot-product operations causing high memory and computation overhead in standard von-Neumann computing platforms. Today, In-Memory Computing (IMC) architectures have been proposed to alleviate the \"memory-wall bottleneck\" prevalent in von-Neumann architectures. Although recent works have proposed IMC-bas"},"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":"2210.12899","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NE","submitted_at":"2022-10-24T01:07:17Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"3a087cf67d1b410655cd9f209155397add14a45bdc1b502a0d30a5783b3fbf76","abstract_canon_sha256":"9ec71fe6f14c35fbd8a1c5fdc4d4053062aa58b255f604b650e77dfb1a38c4d1"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:09:34.001366Z","signature_b64":"eoFF7wQxFM/zDUvNYq0kdwB77Ac4v40g1PVXY08HS35rcWvALINjsHzqDGN/QOpgvIbpe9h4GseDygbm72kfBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"897101df27e83f85641a2e7710b7065d5ec7424d283f50107be99273b6ad95d8","last_reissued_at":"2026-07-05T05:09:34.000986Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:09:34.000986Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"SpikeSim: An end-to-end Compute-in-Memory Hardware Evaluation Tool for Benchmarking Spiking Neural Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"cs.NE","authors_text":"Abhiroop Bhattacharjee, Abhishek Moitra, Gokul Krishnan, Priyadarshini Panda, Runcong Kuang, Yu Cao","submitted_at":"2022-10-24T01:07:17Z","abstract_excerpt":"SNNs are an active research domain towards energy efficient machine intelligence. Compared to conventional ANNs, SNNs use temporal spike data and bio-plausible neuronal activation functions such as Leaky-Integrate Fire/Integrate Fire (LIF/IF) for data processing. However, SNNs incur significant dot-product operations causing high memory and computation overhead in standard von-Neumann computing platforms. Today, In-Memory Computing (IMC) architectures have been proposed to alleviate the \"memory-wall bottleneck\" prevalent in von-Neumann architectures. Although recent works have proposed IMC-bas"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2210.12899","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/2210.12899/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":"2210.12899","created_at":"2026-07-05T05:09:34.001046+00:00"},{"alias_kind":"arxiv_version","alias_value":"2210.12899v1","created_at":"2026-07-05T05:09:34.001046+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2210.12899","created_at":"2026-07-05T05:09:34.001046+00:00"},{"alias_kind":"pith_short_12","alias_value":"RFYQDXZH5A7Y","created_at":"2026-07-05T05:09:34.001046+00:00"},{"alias_kind":"pith_short_16","alias_value":"RFYQDXZH5A7YKZA2","created_at":"2026-07-05T05:09:34.001046+00:00"},{"alias_kind":"pith_short_8","alias_value":"RFYQDXZH","created_at":"2026-07-05T05:09:34.001046+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/RFYQDXZH5A7YKZA2FZ3RBNYGLV","json":"https://pith.science/pith/RFYQDXZH5A7YKZA2FZ3RBNYGLV.json","graph_json":"https://pith.science/api/pith-number/RFYQDXZH5A7YKZA2FZ3RBNYGLV/graph.json","events_json":"https://pith.science/api/pith-number/RFYQDXZH5A7YKZA2FZ3RBNYGLV/events.json","paper":"https://pith.science/paper/RFYQDXZH"},"agent_actions":{"view_html":"https://pith.science/pith/RFYQDXZH5A7YKZA2FZ3RBNYGLV","download_json":"https://pith.science/pith/RFYQDXZH5A7YKZA2FZ3RBNYGLV.json","view_paper":"https://pith.science/paper/RFYQDXZH","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2210.12899&json=true","fetch_graph":"https://pith.science/api/pith-number/RFYQDXZH5A7YKZA2FZ3RBNYGLV/graph.json","fetch_events":"https://pith.science/api/pith-number/RFYQDXZH5A7YKZA2FZ3RBNYGLV/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/RFYQDXZH5A7YKZA2FZ3RBNYGLV/action/timestamp_anchor","attest_storage":"https://pith.science/pith/RFYQDXZH5A7YKZA2FZ3RBNYGLV/action/storage_attestation","attest_author":"https://pith.science/pith/RFYQDXZH5A7YKZA2FZ3RBNYGLV/action/author_attestation","sign_citation":"https://pith.science/pith/RFYQDXZH5A7YKZA2FZ3RBNYGLV/action/citation_signature","submit_replication":"https://pith.science/pith/RFYQDXZH5A7YKZA2FZ3RBNYGLV/action/replication_record"}},"created_at":"2026-07-05T05:09:34.001046+00:00","updated_at":"2026-07-05T05:09:34.001046+00:00"}