{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2016:XXTSGZM5QUDWUEAFXN25VK5657","short_pith_number":"pith:XXTSGZM5","schema_version":"1.0","canonical_sha256":"bde723659d85076a1005bb75daabbeefe38e134fb40ee8f17297bb70cafcbc44","source":{"kind":"arxiv","id":"1609.09296","version":1},"attestation_state":"computed","paper":{"title":"Comprehensive Evaluation of OpenCL-based Convolutional Neural Network Accelerators in Xilinx and Altera FPGAs","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.DC"],"primary_cat":"cs.CV","authors_text":"A. Linares-Barranco, A. Rios-Navarro, Deepak Kadetotad, Jae-sun Seo, Minkyu Kim, R. Tapiador","submitted_at":"2016-09-29T11:03:21Z","abstract_excerpt":"Deep learning has significantly advanced the state of the art in artificial intelligence, gaining wide popularity from both industry and academia. Special interest is around Convolutional Neural Networks (CNN), which take inspiration from the hierarchical structure of the visual cortex, to form deep layers of convolutional operations, along with fully connected classifiers. Hardware implementations of these deep CNN architectures are challenged with memory bottlenecks that require many convolution and fully-connected layers demanding large amount of communication for parallel computation. Mult"},"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":"1609.09296","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-09-29T11:03:21Z","cross_cats_sorted":["cs.DC"],"title_canon_sha256":"761ce8ff786f971c62bdd98bcabaa0ab3850c9c11246301c69782c1ab508110e","abstract_canon_sha256":"58e6567fa2b04684042215809698a5c36bbbf74381e88322b0f75a734365bad8"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:03:38.249716Z","signature_b64":"XlKwYMFkhjiYVkLEGJw6UmpabZNsu3zxEBn3c3OzQvJXp1c4aJ25+orSuU2svHBaBxMfku1UXnGtRdr9DhqLAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"bde723659d85076a1005bb75daabbeefe38e134fb40ee8f17297bb70cafcbc44","last_reissued_at":"2026-05-18T01:03:38.249250Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:03:38.249250Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Comprehensive Evaluation of OpenCL-based Convolutional Neural Network Accelerators in Xilinx and Altera FPGAs","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.DC"],"primary_cat":"cs.CV","authors_text":"A. Linares-Barranco, A. Rios-Navarro, Deepak Kadetotad, Jae-sun Seo, Minkyu Kim, R. Tapiador","submitted_at":"2016-09-29T11:03:21Z","abstract_excerpt":"Deep learning has significantly advanced the state of the art in artificial intelligence, gaining wide popularity from both industry and academia. Special interest is around Convolutional Neural Networks (CNN), which take inspiration from the hierarchical structure of the visual cortex, to form deep layers of convolutional operations, along with fully connected classifiers. Hardware implementations of these deep CNN architectures are challenged with memory bottlenecks that require many convolution and fully-connected layers demanding large amount of communication for parallel computation. Mult"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1609.09296","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":"1609.09296","created_at":"2026-05-18T01:03:38.249313+00:00"},{"alias_kind":"arxiv_version","alias_value":"1609.09296v1","created_at":"2026-05-18T01:03:38.249313+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1609.09296","created_at":"2026-05-18T01:03:38.249313+00:00"},{"alias_kind":"pith_short_12","alias_value":"XXTSGZM5QUDW","created_at":"2026-05-18T12:30:51.357362+00:00"},{"alias_kind":"pith_short_16","alias_value":"XXTSGZM5QUDWUEAF","created_at":"2026-05-18T12:30:51.357362+00:00"},{"alias_kind":"pith_short_8","alias_value":"XXTSGZM5","created_at":"2026-05-18T12:30:51.357362+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/XXTSGZM5QUDWUEAFXN25VK5657","json":"https://pith.science/pith/XXTSGZM5QUDWUEAFXN25VK5657.json","graph_json":"https://pith.science/api/pith-number/XXTSGZM5QUDWUEAFXN25VK5657/graph.json","events_json":"https://pith.science/api/pith-number/XXTSGZM5QUDWUEAFXN25VK5657/events.json","paper":"https://pith.science/paper/XXTSGZM5"},"agent_actions":{"view_html":"https://pith.science/pith/XXTSGZM5QUDWUEAFXN25VK5657","download_json":"https://pith.science/pith/XXTSGZM5QUDWUEAFXN25VK5657.json","view_paper":"https://pith.science/paper/XXTSGZM5","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1609.09296&json=true","fetch_graph":"https://pith.science/api/pith-number/XXTSGZM5QUDWUEAFXN25VK5657/graph.json","fetch_events":"https://pith.science/api/pith-number/XXTSGZM5QUDWUEAFXN25VK5657/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/XXTSGZM5QUDWUEAFXN25VK5657/action/timestamp_anchor","attest_storage":"https://pith.science/pith/XXTSGZM5QUDWUEAFXN25VK5657/action/storage_attestation","attest_author":"https://pith.science/pith/XXTSGZM5QUDWUEAFXN25VK5657/action/author_attestation","sign_citation":"https://pith.science/pith/XXTSGZM5QUDWUEAFXN25VK5657/action/citation_signature","submit_replication":"https://pith.science/pith/XXTSGZM5QUDWUEAFXN25VK5657/action/replication_record"}},"created_at":"2026-05-18T01:03:38.249313+00:00","updated_at":"2026-05-18T01:03:38.249313+00:00"}