{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:GRGG2BVRDNYMCI5NSG7HERAKC3","short_pith_number":"pith:GRGG2BVR","canonical_record":{"source":{"id":"2412.05302","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AR","submitted_at":"2024-11-26T09:41:26Z","cross_cats_sorted":["cs.DC","cs.LG"],"title_canon_sha256":"39411fcc3e79a5a13b971ff721c25699ba8fd4176331eb3e294a3d2c21188a69","abstract_canon_sha256":"f6e1d778a4a1c741ab66c6497c577afc4ca19c5f00ba68ac1478303d154f461f"},"schema_version":"1.0"},"canonical_sha256":"344c6d06b11b70c123ad91be72440a16d09bb4a71e3567e3e2c378d2912e8150","source":{"kind":"arxiv","id":"2412.05302","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2412.05302","created_at":"2026-07-05T09:55:14Z"},{"alias_kind":"arxiv_version","alias_value":"2412.05302v3","created_at":"2026-07-05T09:55:14Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2412.05302","created_at":"2026-07-05T09:55:14Z"},{"alias_kind":"pith_short_12","alias_value":"GRGG2BVRDNYM","created_at":"2026-07-05T09:55:14Z"},{"alias_kind":"pith_short_16","alias_value":"GRGG2BVRDNYMCI5N","created_at":"2026-07-05T09:55:14Z"},{"alias_kind":"pith_short_8","alias_value":"GRGG2BVR","created_at":"2026-07-05T09:55:14Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:GRGG2BVRDNYMCI5NSG7HERAKC3","target":"record","payload":{"canonical_record":{"source":{"id":"2412.05302","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AR","submitted_at":"2024-11-26T09:41:26Z","cross_cats_sorted":["cs.DC","cs.LG"],"title_canon_sha256":"39411fcc3e79a5a13b971ff721c25699ba8fd4176331eb3e294a3d2c21188a69","abstract_canon_sha256":"f6e1d778a4a1c741ab66c6497c577afc4ca19c5f00ba68ac1478303d154f461f"},"schema_version":"1.0"},"canonical_sha256":"344c6d06b11b70c123ad91be72440a16d09bb4a71e3567e3e2c378d2912e8150","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:55:14.784308Z","signature_b64":"MwXtwIKxbVFc8ByrYhSRp7vySOiH6rT8slwxXZUi7GvsytbJQdXTTNo05pjQ85OuTI6lv9sgi7kaqqzmiRP8DA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"344c6d06b11b70c123ad91be72440a16d09bb4a71e3567e3e2c378d2912e8150","last_reissued_at":"2026-07-05T09:55:14.783877Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:55:14.783877Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2412.05302","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T09:55:14Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"lOdCziU4LvH3EdVN8KaYynVXapDpUEhPettCADP8U/jjTEBKB4Tvc/dhs6WnN32/BbvgRSnaFgk4Qaixc4jUDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-07T08:07:17.151523Z"},"content_sha256":"f4bb03bdfb0687f15274278b855c97857750919e1fe8df714f82af7203757254","schema_version":"1.0","event_id":"sha256:f4bb03bdfb0687f15274278b855c97857750919e1fe8df714f82af7203757254"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:GRGG2BVRDNYMCI5NSG7HERAKC3","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"A High Energy-Efficiency Multi-core Neuromorphic Architecture for Deep SNN Training","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.DC","cs.LG"],"primary_cat":"cs.AR","authors_text":"Guoqi Li, Hongyu Guo, Huihui Zhou, Junchao Zhang, Mingjing Li, Puli Quan, Qingyan Meng, Wei Wang, Wenjie Lin, Xiaofeng Xu, Xiaoxin Cui, Xueke Zhu, Yanyu Lin, Yonghong Tian, Yunhao Ma, Zhengyu Ma, Zhiwei Zhong","submitted_at":"2024-11-26T09:41:26Z","abstract_excerpt":"There is a growing necessity for edge training to adapt to dynamically changing environment. Neuromorphic computing represents a significant pathway for high-efficiency intelligent computation in energy-constrained edges, but existing neuromorphic architectures lack the ability of directly training spiking neural networks (SNNs) based on backpropagation. We develop a multi-core neuromorphic architecture with Feedforward-Propagation, Back-Propagation, and Weight-Gradient engines in each core, supporting high efficient parallel computing at both the engine and core levels. It combines various da"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2412.05302","kind":"arxiv","version":3},"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/2412.05302/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":"2026-07-05T09:55:14Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"6KN8WD2brzOm/eoByDTRwQSuzvyF84SR2RpoDwX7+zglbKe0CBXMHkJytCoJQDwOx5f0S5XH4WhZ5LzAkvC6BQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-07T08:07:17.151919Z"},"content_sha256":"57a2846659eecbdfc5eec14497971e7ab9681f88f9e2ea8e6470f04a944a72c3","schema_version":"1.0","event_id":"sha256:57a2846659eecbdfc5eec14497971e7ab9681f88f9e2ea8e6470f04a944a72c3"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/GRGG2BVRDNYMCI5NSG7HERAKC3/bundle.json","state_url":"https://pith.science/pith/GRGG2BVRDNYMCI5NSG7HERAKC3/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/GRGG2BVRDNYMCI5NSG7HERAKC3/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":"2026-07-07T08:07:17Z","links":{"resolver":"https://pith.science/pith/GRGG2BVRDNYMCI5NSG7HERAKC3","bundle":"https://pith.science/pith/GRGG2BVRDNYMCI5NSG7HERAKC3/bundle.json","state":"https://pith.science/pith/GRGG2BVRDNYMCI5NSG7HERAKC3/state.json","well_known_bundle":"https://pith.science/.well-known/pith/GRGG2BVRDNYMCI5NSG7HERAKC3/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:GRGG2BVRDNYMCI5NSG7HERAKC3","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":"f6e1d778a4a1c741ab66c6497c577afc4ca19c5f00ba68ac1478303d154f461f","cross_cats_sorted":["cs.DC","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AR","submitted_at":"2024-11-26T09:41:26Z","title_canon_sha256":"39411fcc3e79a5a13b971ff721c25699ba8fd4176331eb3e294a3d2c21188a69"},"schema_version":"1.0","source":{"id":"2412.05302","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2412.05302","created_at":"2026-07-05T09:55:14Z"},{"alias_kind":"arxiv_version","alias_value":"2412.05302v3","created_at":"2026-07-05T09:55:14Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2412.05302","created_at":"2026-07-05T09:55:14Z"},{"alias_kind":"pith_short_12","alias_value":"GRGG2BVRDNYM","created_at":"2026-07-05T09:55:14Z"},{"alias_kind":"pith_short_16","alias_value":"GRGG2BVRDNYMCI5N","created_at":"2026-07-05T09:55:14Z"},{"alias_kind":"pith_short_8","alias_value":"GRGG2BVR","created_at":"2026-07-05T09:55:14Z"}],"graph_snapshots":[{"event_id":"sha256:57a2846659eecbdfc5eec14497971e7ab9681f88f9e2ea8e6470f04a944a72c3","target":"graph","created_at":"2026-07-05T09:55:14Z","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/2412.05302/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"There is a growing necessity for edge training to adapt to dynamically changing environment. Neuromorphic computing represents a significant pathway for high-efficiency intelligent computation in energy-constrained edges, but existing neuromorphic architectures lack the ability of directly training spiking neural networks (SNNs) based on backpropagation. We develop a multi-core neuromorphic architecture with Feedforward-Propagation, Back-Propagation, and Weight-Gradient engines in each core, supporting high efficient parallel computing at both the engine and core levels. It combines various da","authors_text":"Guoqi Li, Hongyu Guo, Huihui Zhou, Junchao Zhang, Mingjing Li, Puli Quan, Qingyan Meng, Wei Wang, Wenjie Lin, Xiaofeng Xu, Xiaoxin Cui, Xueke Zhu, Yanyu Lin, Yonghong Tian, Yunhao Ma, Zhengyu Ma, Zhiwei Zhong","cross_cats":["cs.DC","cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AR","submitted_at":"2024-11-26T09:41:26Z","title":"A High Energy-Efficiency Multi-core Neuromorphic Architecture for Deep SNN Training"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2412.05302","kind":"arxiv","version":3},"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:f4bb03bdfb0687f15274278b855c97857750919e1fe8df714f82af7203757254","target":"record","created_at":"2026-07-05T09:55:14Z","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":"f6e1d778a4a1c741ab66c6497c577afc4ca19c5f00ba68ac1478303d154f461f","cross_cats_sorted":["cs.DC","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AR","submitted_at":"2024-11-26T09:41:26Z","title_canon_sha256":"39411fcc3e79a5a13b971ff721c25699ba8fd4176331eb3e294a3d2c21188a69"},"schema_version":"1.0","source":{"id":"2412.05302","kind":"arxiv","version":3}},"canonical_sha256":"344c6d06b11b70c123ad91be72440a16d09bb4a71e3567e3e2c378d2912e8150","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"344c6d06b11b70c123ad91be72440a16d09bb4a71e3567e3e2c378d2912e8150","first_computed_at":"2026-07-05T09:55:14.783877Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T09:55:14.783877Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"MwXtwIKxbVFc8ByrYhSRp7vySOiH6rT8slwxXZUi7GvsytbJQdXTTNo05pjQ85OuTI6lv9sgi7kaqqzmiRP8DA==","signature_status":"signed_v1","signed_at":"2026-07-05T09:55:14.784308Z","signed_message":"canonical_sha256_bytes"},"source_id":"2412.05302","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:f4bb03bdfb0687f15274278b855c97857750919e1fe8df714f82af7203757254","sha256:57a2846659eecbdfc5eec14497971e7ab9681f88f9e2ea8e6470f04a944a72c3"],"state_sha256":"0002e886509b3053aaf2118a6c101bd09f6b50a415143b67ee342efc90612008"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"gQ1cww2BUWDg8FtnYjmjM419+KKv30SlDKQvTAzS44EPWFTxOup6X3lmYb0/4F/eRE9R6xM/9i94lMeBbriPAg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-07T08:07:17.153955Z","bundle_sha256":"a0297522ee5a30e0b581be2d15e7b0a7ae8b82ecebf31e8c64c58587364947ff"}}