{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2014:7T67A26BBLN7V3AYC2MPTZ766X","short_pith_number":"pith:7T67A26B","schema_version":"1.0","canonical_sha256":"fcfdf06bc10adbfaec181698f9e7fef5fb0a3ccdd83c9ed9cad2d41ec0e33cd3","source":{"kind":"arxiv","id":"1412.0595","version":1},"attestation_state":"computed","paper":{"title":"Scalability and Optimization Strategies for GPU Enhanced Neural Networks (GeNN)","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.NE","q-bio.NC"],"primary_cat":"cs.DC","authors_text":"Esin Yavuz, Naresh Balaji, Thomas Nowotny","submitted_at":"2014-12-01T19:12:54Z","abstract_excerpt":"Simulation of spiking neural networks has been traditionally done on high-performance supercomputers or large-scale clusters. Utilizing the parallel nature of neural network computation algorithms, GeNN (GPU Enhanced Neural Network) provides a simulation environment that performs on General Purpose NVIDIA GPUs with a code generation based approach. GeNN allows the users to design and simulate neural networks by specifying the populations of neurons at different stages, their synapse connection densities and the model of individual neurons. In this report we describe work on how to scale synapt"},"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":"1412.0595","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.DC","submitted_at":"2014-12-01T19:12:54Z","cross_cats_sorted":["cs.NE","q-bio.NC"],"title_canon_sha256":"078464ffe9b3877b84e697e411b40dd3df4baf44e93243035d74b286c83b72a2","abstract_canon_sha256":"ffb720c3d0804b0ee010842faf9bc1a35a65abbb4cb81b31eca4f5a0a2a49404"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T02:32:23.030724Z","signature_b64":"jFNbJA5JEjsssd59V0VD7YMQDJIpHNxLP8J9NCfM7YD0GJ+0xgmcutU7Y7lxhWgAzdUBj0GOFmM4/dqSiMpmCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"fcfdf06bc10adbfaec181698f9e7fef5fb0a3ccdd83c9ed9cad2d41ec0e33cd3","last_reissued_at":"2026-05-18T02:32:23.030298Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T02:32:23.030298Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Scalability and Optimization Strategies for GPU Enhanced Neural Networks (GeNN)","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.NE","q-bio.NC"],"primary_cat":"cs.DC","authors_text":"Esin Yavuz, Naresh Balaji, Thomas Nowotny","submitted_at":"2014-12-01T19:12:54Z","abstract_excerpt":"Simulation of spiking neural networks has been traditionally done on high-performance supercomputers or large-scale clusters. Utilizing the parallel nature of neural network computation algorithms, GeNN (GPU Enhanced Neural Network) provides a simulation environment that performs on General Purpose NVIDIA GPUs with a code generation based approach. GeNN allows the users to design and simulate neural networks by specifying the populations of neurons at different stages, their synapse connection densities and the model of individual neurons. In this report we describe work on how to scale synapt"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1412.0595","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":""},"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":"1412.0595","created_at":"2026-05-18T02:32:23.030372+00:00"},{"alias_kind":"arxiv_version","alias_value":"1412.0595v1","created_at":"2026-05-18T02:32:23.030372+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1412.0595","created_at":"2026-05-18T02:32:23.030372+00:00"},{"alias_kind":"pith_short_12","alias_value":"7T67A26BBLN7","created_at":"2026-05-18T12:28:19.803747+00:00"},{"alias_kind":"pith_short_16","alias_value":"7T67A26BBLN7V3AY","created_at":"2026-05-18T12:28:19.803747+00:00"},{"alias_kind":"pith_short_8","alias_value":"7T67A26B","created_at":"2026-05-18T12:28:19.803747+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/7T67A26BBLN7V3AYC2MPTZ766X","json":"https://pith.science/pith/7T67A26BBLN7V3AYC2MPTZ766X.json","graph_json":"https://pith.science/api/pith-number/7T67A26BBLN7V3AYC2MPTZ766X/graph.json","events_json":"https://pith.science/api/pith-number/7T67A26BBLN7V3AYC2MPTZ766X/events.json","paper":"https://pith.science/paper/7T67A26B"},"agent_actions":{"view_html":"https://pith.science/pith/7T67A26BBLN7V3AYC2MPTZ766X","download_json":"https://pith.science/pith/7T67A26BBLN7V3AYC2MPTZ766X.json","view_paper":"https://pith.science/paper/7T67A26B","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1412.0595&json=true","fetch_graph":"https://pith.science/api/pith-number/7T67A26BBLN7V3AYC2MPTZ766X/graph.json","fetch_events":"https://pith.science/api/pith-number/7T67A26BBLN7V3AYC2MPTZ766X/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/7T67A26BBLN7V3AYC2MPTZ766X/action/timestamp_anchor","attest_storage":"https://pith.science/pith/7T67A26BBLN7V3AYC2MPTZ766X/action/storage_attestation","attest_author":"https://pith.science/pith/7T67A26BBLN7V3AYC2MPTZ766X/action/author_attestation","sign_citation":"https://pith.science/pith/7T67A26BBLN7V3AYC2MPTZ766X/action/citation_signature","submit_replication":"https://pith.science/pith/7T67A26BBLN7V3AYC2MPTZ766X/action/replication_record"}},"created_at":"2026-05-18T02:32:23.030372+00:00","updated_at":"2026-05-18T02:32:23.030372+00:00"}