{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:OJM73OPPB5FXS4O2STH3WAA5RR","short_pith_number":"pith:OJM73OPP","canonical_record":{"source":{"id":"2606.30926","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AR","submitted_at":"2026-06-29T21:21:18Z","cross_cats_sorted":[],"title_canon_sha256":"43c7d661b123968017b6ef761bbbe44b6c542f91e586d55824738f80944e7204","abstract_canon_sha256":"470c055d49efa7a35fac4b5a7a2b7e490f38158616581249e544825153e56973"},"schema_version":"1.0"},"canonical_sha256":"7259fdb9ef0f4b7971da94cfbb001d8c726d340b53226dfebcc23acf5487f7aa","source":{"kind":"arxiv","id":"2606.30926","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2606.30926","created_at":"2026-07-01T00:17:21Z"},{"alias_kind":"arxiv_version","alias_value":"2606.30926v1","created_at":"2026-07-01T00:17:21Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.30926","created_at":"2026-07-01T00:17:21Z"},{"alias_kind":"pith_short_12","alias_value":"OJM73OPPB5FX","created_at":"2026-07-01T00:17:21Z"},{"alias_kind":"pith_short_16","alias_value":"OJM73OPPB5FXS4O2","created_at":"2026-07-01T00:17:21Z"},{"alias_kind":"pith_short_8","alias_value":"OJM73OPP","created_at":"2026-07-01T00:17:21Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:OJM73OPPB5FXS4O2STH3WAA5RR","target":"record","payload":{"canonical_record":{"source":{"id":"2606.30926","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AR","submitted_at":"2026-06-29T21:21:18Z","cross_cats_sorted":[],"title_canon_sha256":"43c7d661b123968017b6ef761bbbe44b6c542f91e586d55824738f80944e7204","abstract_canon_sha256":"470c055d49efa7a35fac4b5a7a2b7e490f38158616581249e544825153e56973"},"schema_version":"1.0"},"canonical_sha256":"7259fdb9ef0f4b7971da94cfbb001d8c726d340b53226dfebcc23acf5487f7aa","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-01T00:17:21.847485Z","signature_b64":"Z/8eJVIDa+Zf6n8fKXtWrsOz+LcWA2g+fETbuSiP0jzzvDgQWDpQ6DBDH5v8n8cIyV0fx55DztRoZRddaV//Dw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7259fdb9ef0f4b7971da94cfbb001d8c726d340b53226dfebcc23acf5487f7aa","last_reissued_at":"2026-07-01T00:17:21.846710Z","signature_status":"signed_v1","first_computed_at":"2026-07-01T00:17:21.846710Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2606.30926","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-01T00:17:21Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"3TzLx5ZekOvcPMaDImuqvj9NsKSmJC+64pTljM93CqPlYjBLXMOVbgEedVua/xps/gddrLQkGGGmudC6ySlYCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-06T08:56:41.732768Z"},"content_sha256":"585c3f7899e448fadf078451e55edb592c45a02050df613c49ef459263b7d697","schema_version":"1.0","event_id":"sha256:585c3f7899e448fadf078451e55edb592c45a02050df613c49ef459263b7d697"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:OJM73OPPB5FXS4O2STH3WAA5RR","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"SpikON: A Dual-Parallel and Efficient Accelerator for Online Spiking Neural Networks Learning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.AR","authors_text":"Peilin Chen, Xiaoxuan Yang","submitted_at":"2026-06-29T21:21:18Z","abstract_excerpt":"Spiking neural networks (SNNs) have emerged as a promising paradigm for energy-efficient brain-inspired computing. However, existing online unsupervised SNN learning suffers from low training accuracy and poor scalability. Although current online supervised learning algorithms perform well on large-scale datasets and networks, the non-hardware-friendly operations hinder efficient edge deployment. In this work, we propose SpikON, the first algorithm-hardware co-design framework for efficient and scalable end-to-end online supervised SNN learning. We first propose the learnable threshold through"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.30926","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/2606.30926/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-01T00:17:21Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"/1h+0Pnsowcw1Rk6XIvAblEVWAEfr1+QDhXLx4dEv1a66ePgiN4u74RNLSWThY15Pzgjj/b8Yt0nVjNrp8yCCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-06T08:56:41.733352Z"},"content_sha256":"04af70adcfbbb29c475bc795cdcd910fcf06abdd49e40f4db09e75ac65d804e3","schema_version":"1.0","event_id":"sha256:04af70adcfbbb29c475bc795cdcd910fcf06abdd49e40f4db09e75ac65d804e3"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/OJM73OPPB5FXS4O2STH3WAA5RR/bundle.json","state_url":"https://pith.science/pith/OJM73OPPB5FXS4O2STH3WAA5RR/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/OJM73OPPB5FXS4O2STH3WAA5RR/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-07-06T08:56:41Z","links":{"resolver":"https://pith.science/pith/OJM73OPPB5FXS4O2STH3WAA5RR","bundle":"https://pith.science/pith/OJM73OPPB5FXS4O2STH3WAA5RR/bundle.json","state":"https://pith.science/pith/OJM73OPPB5FXS4O2STH3WAA5RR/state.json","well_known_bundle":"https://pith.science/.well-known/pith/OJM73OPPB5FXS4O2STH3WAA5RR/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:OJM73OPPB5FXS4O2STH3WAA5RR","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"470c055d49efa7a35fac4b5a7a2b7e490f38158616581249e544825153e56973","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AR","submitted_at":"2026-06-29T21:21:18Z","title_canon_sha256":"43c7d661b123968017b6ef761bbbe44b6c542f91e586d55824738f80944e7204"},"schema_version":"1.0","source":{"id":"2606.30926","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2606.30926","created_at":"2026-07-01T00:17:21Z"},{"alias_kind":"arxiv_version","alias_value":"2606.30926v1","created_at":"2026-07-01T00:17:21Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.30926","created_at":"2026-07-01T00:17:21Z"},{"alias_kind":"pith_short_12","alias_value":"OJM73OPPB5FX","created_at":"2026-07-01T00:17:21Z"},{"alias_kind":"pith_short_16","alias_value":"OJM73OPPB5FXS4O2","created_at":"2026-07-01T00:17:21Z"},{"alias_kind":"pith_short_8","alias_value":"OJM73OPP","created_at":"2026-07-01T00:17:21Z"}],"graph_snapshots":[{"event_id":"sha256:04af70adcfbbb29c475bc795cdcd910fcf06abdd49e40f4db09e75ac65d804e3","target":"graph","created_at":"2026-07-01T00:17:21Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2606.30926/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Spiking neural networks (SNNs) have emerged as a promising paradigm for energy-efficient brain-inspired computing. However, existing online unsupervised SNN learning suffers from low training accuracy and poor scalability. Although current online supervised learning algorithms perform well on large-scale datasets and networks, the non-hardware-friendly operations hinder efficient edge deployment. In this work, we propose SpikON, the first algorithm-hardware co-design framework for efficient and scalable end-to-end online supervised SNN learning. We first propose the learnable threshold through","authors_text":"Peilin Chen, Xiaoxuan Yang","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AR","submitted_at":"2026-06-29T21:21:18Z","title":"SpikON: A Dual-Parallel and Efficient Accelerator for Online Spiking Neural Networks Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.30926","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:585c3f7899e448fadf078451e55edb592c45a02050df613c49ef459263b7d697","target":"record","created_at":"2026-07-01T00:17:21Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"470c055d49efa7a35fac4b5a7a2b7e490f38158616581249e544825153e56973","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AR","submitted_at":"2026-06-29T21:21:18Z","title_canon_sha256":"43c7d661b123968017b6ef761bbbe44b6c542f91e586d55824738f80944e7204"},"schema_version":"1.0","source":{"id":"2606.30926","kind":"arxiv","version":1}},"canonical_sha256":"7259fdb9ef0f4b7971da94cfbb001d8c726d340b53226dfebcc23acf5487f7aa","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"7259fdb9ef0f4b7971da94cfbb001d8c726d340b53226dfebcc23acf5487f7aa","first_computed_at":"2026-07-01T00:17:21.846710Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-01T00:17:21.846710Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Z/8eJVIDa+Zf6n8fKXtWrsOz+LcWA2g+fETbuSiP0jzzvDgQWDpQ6DBDH5v8n8cIyV0fx55DztRoZRddaV//Dw==","signature_status":"signed_v1","signed_at":"2026-07-01T00:17:21.847485Z","signed_message":"canonical_sha256_bytes"},"source_id":"2606.30926","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:585c3f7899e448fadf078451e55edb592c45a02050df613c49ef459263b7d697","sha256:04af70adcfbbb29c475bc795cdcd910fcf06abdd49e40f4db09e75ac65d804e3"],"state_sha256":"c090403a593605c5d475d8fdbd903d4fb3a42910bb61a2ab0c461c45192b3d67"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"bqx3woZtxlvYIm9V+SWkn30OAd7A95ubklp+KsmrihFXTqRXUlQ9cU/ujBDQA0Phq5/GvF2ZfBXaz6BRTg+8DA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-06T08:56:41.735583Z","bundle_sha256":"78178b42f4d543aebf2cb114bb9027b4e0fcbd9d1970467057878e643435ddd2"}}