{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:7CM6IUZ54KCME74Q3FBZZHXDOK","short_pith_number":"pith:7CM6IUZ5","schema_version":"1.0","canonical_sha256":"f899e4533de284c27f90d9439c9ee372a37bed50a5d63fad2e306a93f7a90fe3","source":{"kind":"arxiv","id":"2205.14248","version":1},"attestation_state":"computed","paper":{"title":"Towards a Design Framework for TNN-Based Neuromorphic Sensory Processing Units","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AR","cs.NE"],"primary_cat":"cs.ET","authors_text":"John Paul Shen, Prabhu Vellaisamy","submitted_at":"2022-05-27T21:51:05Z","abstract_excerpt":"Temporal Neural Networks (TNNs) are spiking neural networks that exhibit brain-like sensory processing with high energy efficiency. This work presents the ongoing research towards developing a custom design framework for designing efficient application-specific TNN-based Neuromorphic Sensory Processing Units (NSPUs). This paper examines previous works on NSPU designs for UCR time-series clustering and MNIST image classification applications. Current ideas for a custom design framework and tools that enable efficient software-to-hardware design flow for rapid design space exploration of applica"},"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":"2205.14248","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.ET","submitted_at":"2022-05-27T21:51:05Z","cross_cats_sorted":["cs.AR","cs.NE"],"title_canon_sha256":"d7a5f4adb96ed9d62fb378d833b52b94f47a979e46f5754a60013747d7d8d0c1","abstract_canon_sha256":"6b02f45472fc3f6402bab9cdabd3d09971d34649efab0c05b5d4eca4307b4693"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:27:15.415963Z","signature_b64":"i0Q/0J7aiJpNUiP0jhGHR4STp7iA1tTx6X2beS/e8R/6xYWfOGkjnCRK9Tn1ZvvDLaV6TVxTrrzyp46trzRMBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f899e4533de284c27f90d9439c9ee372a37bed50a5d63fad2e306a93f7a90fe3","last_reissued_at":"2026-07-05T04:27:15.415340Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:27:15.415340Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Towards a Design Framework for TNN-Based Neuromorphic Sensory Processing Units","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AR","cs.NE"],"primary_cat":"cs.ET","authors_text":"John Paul Shen, Prabhu Vellaisamy","submitted_at":"2022-05-27T21:51:05Z","abstract_excerpt":"Temporal Neural Networks (TNNs) are spiking neural networks that exhibit brain-like sensory processing with high energy efficiency. This work presents the ongoing research towards developing a custom design framework for designing efficient application-specific TNN-based Neuromorphic Sensory Processing Units (NSPUs). This paper examines previous works on NSPU designs for UCR time-series clustering and MNIST image classification applications. Current ideas for a custom design framework and tools that enable efficient software-to-hardware design flow for rapid design space exploration of applica"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2205.14248","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/2205.14248/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":"2205.14248","created_at":"2026-07-05T04:27:15.415409+00:00"},{"alias_kind":"arxiv_version","alias_value":"2205.14248v1","created_at":"2026-07-05T04:27:15.415409+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2205.14248","created_at":"2026-07-05T04:27:15.415409+00:00"},{"alias_kind":"pith_short_12","alias_value":"7CM6IUZ54KCM","created_at":"2026-07-05T04:27:15.415409+00:00"},{"alias_kind":"pith_short_16","alias_value":"7CM6IUZ54KCME74Q","created_at":"2026-07-05T04:27:15.415409+00:00"},{"alias_kind":"pith_short_8","alias_value":"7CM6IUZ5","created_at":"2026-07-05T04:27:15.415409+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2412.17977","citing_title":"TNNGen: Automated Design of Neuromorphic Sensory Processing Units for Time-Series Clustering","ref_index":19,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/7CM6IUZ54KCME74Q3FBZZHXDOK","json":"https://pith.science/pith/7CM6IUZ54KCME74Q3FBZZHXDOK.json","graph_json":"https://pith.science/api/pith-number/7CM6IUZ54KCME74Q3FBZZHXDOK/graph.json","events_json":"https://pith.science/api/pith-number/7CM6IUZ54KCME74Q3FBZZHXDOK/events.json","paper":"https://pith.science/paper/7CM6IUZ5"},"agent_actions":{"view_html":"https://pith.science/pith/7CM6IUZ54KCME74Q3FBZZHXDOK","download_json":"https://pith.science/pith/7CM6IUZ54KCME74Q3FBZZHXDOK.json","view_paper":"https://pith.science/paper/7CM6IUZ5","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2205.14248&json=true","fetch_graph":"https://pith.science/api/pith-number/7CM6IUZ54KCME74Q3FBZZHXDOK/graph.json","fetch_events":"https://pith.science/api/pith-number/7CM6IUZ54KCME74Q3FBZZHXDOK/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/7CM6IUZ54KCME74Q3FBZZHXDOK/action/timestamp_anchor","attest_storage":"https://pith.science/pith/7CM6IUZ54KCME74Q3FBZZHXDOK/action/storage_attestation","attest_author":"https://pith.science/pith/7CM6IUZ54KCME74Q3FBZZHXDOK/action/author_attestation","sign_citation":"https://pith.science/pith/7CM6IUZ54KCME74Q3FBZZHXDOK/action/citation_signature","submit_replication":"https://pith.science/pith/7CM6IUZ54KCME74Q3FBZZHXDOK/action/replication_record"}},"created_at":"2026-07-05T04:27:15.415409+00:00","updated_at":"2026-07-05T04:27:15.415409+00:00"}