{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2016:5AVEFTGVYQDZU76UM746IT2EEC","short_pith_number":"pith:5AVEFTGV","schema_version":"1.0","canonical_sha256":"e82a42ccd5c4079a7fd467f9e44f4420b4968f51cfb06afaad5b79b7f5b95bc3","source":{"kind":"arxiv","id":"1609.09671","version":1},"attestation_state":"computed","paper":{"title":"Caffeinated FPGAs: FPGA Framework For Convolutional Neural Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.DC"],"primary_cat":"cs.CV","authors_text":"Graham Taylor, Griffin Lacey, Jasmina Vasiljevic, Paul Chow, Roberto DiCecco, Shawki Areibi","submitted_at":"2016-09-30T11:13:21Z","abstract_excerpt":"Convolutional Neural Networks (CNNs) have gained significant traction in the field of machine learning, particularly due to their high accuracy in visual recognition. Recent works have pushed the performance of GPU implementations of CNNs to significantly improve their classification and training times. With these improvements, many frameworks have become available for implementing CNNs on both CPUs and GPUs, with no support for FPGA implementations. In this work we present a modified version of the popular CNN framework Caffe, with FPGA support. This allows for classification using CNN models"},"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":"1609.09671","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-09-30T11:13:21Z","cross_cats_sorted":["cs.DC"],"title_canon_sha256":"b14cdd76810ade8e32c99b5adc11c9763b1429a1267760165b21663bdf83a6b5","abstract_canon_sha256":"01b3a778f933da354fbcef758467d189b8b1110f7dfb8346c4007dbebaf23c36"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:03:35.342713Z","signature_b64":"XEjYGIcNYlMeZv8ipuD6bQiVJkvX/FPJBvTeWPnUq/JTvSP4GJrkE6gWoa48c7SeeRCxtLzbpzjCoN86CNsdDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e82a42ccd5c4079a7fd467f9e44f4420b4968f51cfb06afaad5b79b7f5b95bc3","last_reissued_at":"2026-05-18T01:03:35.342017Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:03:35.342017Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Caffeinated FPGAs: FPGA Framework For Convolutional Neural Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.DC"],"primary_cat":"cs.CV","authors_text":"Graham Taylor, Griffin Lacey, Jasmina Vasiljevic, Paul Chow, Roberto DiCecco, Shawki Areibi","submitted_at":"2016-09-30T11:13:21Z","abstract_excerpt":"Convolutional Neural Networks (CNNs) have gained significant traction in the field of machine learning, particularly due to their high accuracy in visual recognition. Recent works have pushed the performance of GPU implementations of CNNs to significantly improve their classification and training times. With these improvements, many frameworks have become available for implementing CNNs on both CPUs and GPUs, with no support for FPGA implementations. In this work we present a modified version of the popular CNN framework Caffe, with FPGA support. This allows for classification using CNN models"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1609.09671","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":"1609.09671","created_at":"2026-05-18T01:03:35.342149+00:00"},{"alias_kind":"arxiv_version","alias_value":"1609.09671v1","created_at":"2026-05-18T01:03:35.342149+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1609.09671","created_at":"2026-05-18T01:03:35.342149+00:00"},{"alias_kind":"pith_short_12","alias_value":"5AVEFTGVYQDZ","created_at":"2026-05-18T12:29:58.707656+00:00"},{"alias_kind":"pith_short_16","alias_value":"5AVEFTGVYQDZU76U","created_at":"2026-05-18T12:29:58.707656+00:00"},{"alias_kind":"pith_short_8","alias_value":"5AVEFTGV","created_at":"2026-05-18T12:29:58.707656+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/5AVEFTGVYQDZU76UM746IT2EEC","json":"https://pith.science/pith/5AVEFTGVYQDZU76UM746IT2EEC.json","graph_json":"https://pith.science/api/pith-number/5AVEFTGVYQDZU76UM746IT2EEC/graph.json","events_json":"https://pith.science/api/pith-number/5AVEFTGVYQDZU76UM746IT2EEC/events.json","paper":"https://pith.science/paper/5AVEFTGV"},"agent_actions":{"view_html":"https://pith.science/pith/5AVEFTGVYQDZU76UM746IT2EEC","download_json":"https://pith.science/pith/5AVEFTGVYQDZU76UM746IT2EEC.json","view_paper":"https://pith.science/paper/5AVEFTGV","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1609.09671&json=true","fetch_graph":"https://pith.science/api/pith-number/5AVEFTGVYQDZU76UM746IT2EEC/graph.json","fetch_events":"https://pith.science/api/pith-number/5AVEFTGVYQDZU76UM746IT2EEC/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/5AVEFTGVYQDZU76UM746IT2EEC/action/timestamp_anchor","attest_storage":"https://pith.science/pith/5AVEFTGVYQDZU76UM746IT2EEC/action/storage_attestation","attest_author":"https://pith.science/pith/5AVEFTGVYQDZU76UM746IT2EEC/action/author_attestation","sign_citation":"https://pith.science/pith/5AVEFTGVYQDZU76UM746IT2EEC/action/citation_signature","submit_replication":"https://pith.science/pith/5AVEFTGVYQDZU76UM746IT2EEC/action/replication_record"}},"created_at":"2026-05-18T01:03:35.342149+00:00","updated_at":"2026-05-18T01:03:35.342149+00:00"}