{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:DEARRAMTMC62JD37XGKNRYC6FN","short_pith_number":"pith:DEARRAMT","schema_version":"1.0","canonical_sha256":"190118819360bda48f7fb994d8e05e2b464eebeafcb3a98e2817586749832302","source":{"kind":"arxiv","id":"2607.24396","version":1},"attestation_state":"computed","paper":{"title":"The SpiNNaker2 chip: a many-core platform for flexible and scalable brain-inspired computing","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.AR","cs.DC"],"primary_cat":"cs.ET","authors_text":"Amirhossein Rostami, Andreas Dixius, Bernhard Vogginger, Chen Liu, Christian Mayr, Delong Shang, Dongwei Hu, Felix Neum\\\"arker, Florian Kelber, Gengting Liu, Georg Ellguth, Hector A. Gonzalez, Jim Garside, Johannes Partzsch, Khaleelulla Khan Nazeer, Mantas Mikaitis, Marc Berthel, Marco Stolba, Matthias Jobst, Matthias Lohrmann, Sebastian H\\\"oppner, Sirine Arfa, Stefan Schiefer, Stefan Scholze, Stephan Hartmann, Steve Furber, Thomas Kiel-Hocker, Tilo Schubert, Tim Langer, Yexin Yan","submitted_at":"2026-07-27T13:13:02Z","abstract_excerpt":"In deep learning, efficiency gets more and more important to compensate for the ongoing growth in model sizes and applications. Neuromorphic hardware has long been advocated as an upcoming alternative to deep networks, taking inspiration from the brain for achieving unprecedented energy efficiency. However, demonstrations of these gains only recently began to grow in complexity and real-world applicability. With SpiNNaker2, we present a chip that bridges the gap between deep networks and neuromorphic computing and allows for flexible exploration of computing approaches that combine both worlds"},"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":"2607.24396","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.ET","submitted_at":"2026-07-27T13:13:02Z","cross_cats_sorted":["cs.AI","cs.AR","cs.DC"],"title_canon_sha256":"55f595c384d61590c583d36177929a610a4955435705377de8945de68742142f","abstract_canon_sha256":"293a3ae2a73c49cd565e86f08536cec6f596c1582f75dd78b31ad316d6b20895"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-28T02:24:01.169352Z","signature_b64":"CYpYJDEO3pddi+Vd0ej1cvUx2zkGy2V+va8sH9y/FUoyxKp4YvuRWrPTl5TzC6dMaMetvaCyQASUQH1ncAw2CQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"190118819360bda48f7fb994d8e05e2b464eebeafcb3a98e2817586749832302","last_reissued_at":"2026-07-28T02:24:01.168518Z","signature_status":"signed_v1","first_computed_at":"2026-07-28T02:24:01.168518Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"The SpiNNaker2 chip: a many-core platform for flexible and scalable brain-inspired computing","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.AR","cs.DC"],"primary_cat":"cs.ET","authors_text":"Amirhossein Rostami, Andreas Dixius, Bernhard Vogginger, Chen Liu, Christian Mayr, Delong Shang, Dongwei Hu, Felix Neum\\\"arker, Florian Kelber, Gengting Liu, Georg Ellguth, Hector A. Gonzalez, Jim Garside, Johannes Partzsch, Khaleelulla Khan Nazeer, Mantas Mikaitis, Marc Berthel, Marco Stolba, Matthias Jobst, Matthias Lohrmann, Sebastian H\\\"oppner, Sirine Arfa, Stefan Schiefer, Stefan Scholze, Stephan Hartmann, Steve Furber, Thomas Kiel-Hocker, Tilo Schubert, Tim Langer, Yexin Yan","submitted_at":"2026-07-27T13:13:02Z","abstract_excerpt":"In deep learning, efficiency gets more and more important to compensate for the ongoing growth in model sizes and applications. Neuromorphic hardware has long been advocated as an upcoming alternative to deep networks, taking inspiration from the brain for achieving unprecedented energy efficiency. However, demonstrations of these gains only recently began to grow in complexity and real-world applicability. With SpiNNaker2, we present a chip that bridges the gap between deep networks and neuromorphic computing and allows for flexible exploration of computing approaches that combine both worlds"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.24396","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/2607.24396/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":"2607.24396","created_at":"2026-07-28T02:24:01.168939+00:00"},{"alias_kind":"arxiv_version","alias_value":"2607.24396v1","created_at":"2026-07-28T02:24:01.168939+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.24396","created_at":"2026-07-28T02:24:01.168939+00:00"},{"alias_kind":"pith_short_12","alias_value":"DEARRAMTMC62","created_at":"2026-07-28T02:24:01.168939+00:00"},{"alias_kind":"pith_short_16","alias_value":"DEARRAMTMC62JD37","created_at":"2026-07-28T02:24:01.168939+00:00"},{"alias_kind":"pith_short_8","alias_value":"DEARRAMT","created_at":"2026-07-28T02:24:01.168939+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/DEARRAMTMC62JD37XGKNRYC6FN","json":"https://pith.science/pith/DEARRAMTMC62JD37XGKNRYC6FN.json","graph_json":"https://pith.science/api/pith-number/DEARRAMTMC62JD37XGKNRYC6FN/graph.json","events_json":"https://pith.science/api/pith-number/DEARRAMTMC62JD37XGKNRYC6FN/events.json","paper":"https://pith.science/paper/DEARRAMT"},"agent_actions":{"view_html":"https://pith.science/pith/DEARRAMTMC62JD37XGKNRYC6FN","download_json":"https://pith.science/pith/DEARRAMTMC62JD37XGKNRYC6FN.json","view_paper":"https://pith.science/paper/DEARRAMT","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2607.24396&json=true","fetch_graph":"https://pith.science/api/pith-number/DEARRAMTMC62JD37XGKNRYC6FN/graph.json","fetch_events":"https://pith.science/api/pith-number/DEARRAMTMC62JD37XGKNRYC6FN/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/DEARRAMTMC62JD37XGKNRYC6FN/action/timestamp_anchor","attest_storage":"https://pith.science/pith/DEARRAMTMC62JD37XGKNRYC6FN/action/storage_attestation","attest_author":"https://pith.science/pith/DEARRAMTMC62JD37XGKNRYC6FN/action/author_attestation","sign_citation":"https://pith.science/pith/DEARRAMTMC62JD37XGKNRYC6FN/action/citation_signature","submit_replication":"https://pith.science/pith/DEARRAMTMC62JD37XGKNRYC6FN/action/replication_record"}},"created_at":"2026-07-28T02:24:01.168939+00:00","updated_at":"2026-07-28T02:24:01.168939+00:00"}