{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:BXOILWHFCAIENPNEBINY77DEZP","short_pith_number":"pith:BXOILWHF","schema_version":"1.0","canonical_sha256":"0ddc85d8e5101046bda40a1b8ffc64cbec3f2d7a544886ad0e739b774f6b3e2e","source":{"kind":"arxiv","id":"2103.08392","version":2},"attestation_state":"computed","paper":{"title":"The SpiNNaker 2 Processing Element Architecture for Hybrid Digital Neuromorphic Computing","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.AR","authors_text":"Andreas Dixius, Bernhard Vogginger, Christian Mayr, Dennis Walter, Felix Neum\\\"arker, Florian Kelber, Genting Liu, Georg Ellguth, Jim Garside, Johannes Partzsch, Marco Stolba, Sebastian H\\\"oppner, Stefan Schiefer, Stefan Scholze, Stephan Hartmann, Steve Furber, Thomas Hocker, Yexin Yan","submitted_at":"2021-03-15T13:59:36Z","abstract_excerpt":"This paper introduces the processing element architecture of the second generation SpiNNaker chip, implemented in 22nm FDSOI. On circuit level, the chip features adaptive body biasing for near-threshold operation, and dynamic voltage-and-frequency scaling driven by spiking activity. On system level, processing is centered around an ARM M4 core, similar to the processor-centric architecture of the first generation SpiNNaker. To speed operation of subtasks, we have added accelerators for numerical operations of both spiking (SNN) and rate based (deep) neural networks (DNN). PEs communicate via a"},"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":"2103.08392","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AR","submitted_at":"2021-03-15T13:59:36Z","cross_cats_sorted":[],"title_canon_sha256":"71199c0af1dc63f2ceefa35dabc3c99d9182a3311a7201696c78edf05407d24b","abstract_canon_sha256":"57eda777468f5b3f77b5d1b9c143a31d6ba37f86a5c5a2b239e8bb631373e989"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:48:06.783918Z","signature_b64":"TAvIwinfS+DRvluhSZUqzvn+kHgvVwN/Qib9C8rDAHhbhu5qhG5S2jczSddtKo2xQhhnEUh/PpCzkHtRuhTuDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0ddc85d8e5101046bda40a1b8ffc64cbec3f2d7a544886ad0e739b774f6b3e2e","last_reissued_at":"2026-07-05T04:48:06.783434Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:48:06.783434Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"The SpiNNaker 2 Processing Element Architecture for Hybrid Digital Neuromorphic Computing","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.AR","authors_text":"Andreas Dixius, Bernhard Vogginger, Christian Mayr, Dennis Walter, Felix Neum\\\"arker, Florian Kelber, Genting Liu, Georg Ellguth, Jim Garside, Johannes Partzsch, Marco Stolba, Sebastian H\\\"oppner, Stefan Schiefer, Stefan Scholze, Stephan Hartmann, Steve Furber, Thomas Hocker, Yexin Yan","submitted_at":"2021-03-15T13:59:36Z","abstract_excerpt":"This paper introduces the processing element architecture of the second generation SpiNNaker chip, implemented in 22nm FDSOI. On circuit level, the chip features adaptive body biasing for near-threshold operation, and dynamic voltage-and-frequency scaling driven by spiking activity. On system level, processing is centered around an ARM M4 core, similar to the processor-centric architecture of the first generation SpiNNaker. To speed operation of subtasks, we have added accelerators for numerical operations of both spiking (SNN) and rate based (deep) neural networks (DNN). PEs communicate via a"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2103.08392","kind":"arxiv","version":2},"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/2103.08392/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":"2103.08392","created_at":"2026-07-05T04:48:06.783493+00:00"},{"alias_kind":"arxiv_version","alias_value":"2103.08392v2","created_at":"2026-07-05T04:48:06.783493+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2103.08392","created_at":"2026-07-05T04:48:06.783493+00:00"},{"alias_kind":"pith_short_12","alias_value":"BXOILWHFCAIE","created_at":"2026-07-05T04:48:06.783493+00:00"},{"alias_kind":"pith_short_16","alias_value":"BXOILWHFCAIENPNE","created_at":"2026-07-05T04:48:06.783493+00:00"},{"alias_kind":"pith_short_8","alias_value":"BXOILWHF","created_at":"2026-07-05T04:48:06.783493+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":6,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.23796","citing_title":"UniSpike: Accelerating Spiking Neural Networks on Neuromorphic Systems via Eliminating Address Redundancy","ref_index":23,"is_internal_anchor":false},{"citing_arxiv_id":"2604.03432","citing_title":"YANA: Bridging the Neuromorphic Simulation-to-Hardware Gap","ref_index":10,"is_internal_anchor":false},{"citing_arxiv_id":"2604.27004","citing_title":"EdgeSpike: Spiking Neural Networks for Low-Power Autonomous Sensing in Edge IoT Architectures","ref_index":52,"is_internal_anchor":false},{"citing_arxiv_id":"2604.16474","citing_title":"Full Feature Spiking Neural Network Simulation on Micro-Controllers for Neuromorphic Applications at the Edge","ref_index":28,"is_internal_anchor":false},{"citing_arxiv_id":"2604.18610","citing_title":"SpikeMLLM: Spike-based Multimodal Large Language Models via Modality-Specific Temporal Scales and Temporal Compression","ref_index":18,"is_internal_anchor":false},{"citing_arxiv_id":"2604.08774","citing_title":"Memory Wall is not gone: A Critical Outlook on Memory Architecture in Digital Neuromorphic Computing","ref_index":12,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/BXOILWHFCAIENPNEBINY77DEZP","json":"https://pith.science/pith/BXOILWHFCAIENPNEBINY77DEZP.json","graph_json":"https://pith.science/api/pith-number/BXOILWHFCAIENPNEBINY77DEZP/graph.json","events_json":"https://pith.science/api/pith-number/BXOILWHFCAIENPNEBINY77DEZP/events.json","paper":"https://pith.science/paper/BXOILWHF"},"agent_actions":{"view_html":"https://pith.science/pith/BXOILWHFCAIENPNEBINY77DEZP","download_json":"https://pith.science/pith/BXOILWHFCAIENPNEBINY77DEZP.json","view_paper":"https://pith.science/paper/BXOILWHF","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2103.08392&json=true","fetch_graph":"https://pith.science/api/pith-number/BXOILWHFCAIENPNEBINY77DEZP/graph.json","fetch_events":"https://pith.science/api/pith-number/BXOILWHFCAIENPNEBINY77DEZP/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/BXOILWHFCAIENPNEBINY77DEZP/action/timestamp_anchor","attest_storage":"https://pith.science/pith/BXOILWHFCAIENPNEBINY77DEZP/action/storage_attestation","attest_author":"https://pith.science/pith/BXOILWHFCAIENPNEBINY77DEZP/action/author_attestation","sign_citation":"https://pith.science/pith/BXOILWHFCAIENPNEBINY77DEZP/action/citation_signature","submit_replication":"https://pith.science/pith/BXOILWHFCAIENPNEBINY77DEZP/action/replication_record"}},"created_at":"2026-07-05T04:48:06.783493+00:00","updated_at":"2026-07-05T04:48:06.783493+00:00"}