{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2015:B3IPAZ53VNF3XHNTB23LUEBACY","short_pith_number":"pith:B3IPAZ53","schema_version":"1.0","canonical_sha256":"0ed0f067bbab4bbb9db30eb6ba1020162450de6393b8e6fd45434e69c5f71e68","source":{"kind":"arxiv","id":"1506.09067","version":1},"attestation_state":"computed","paper":{"title":"The Potential of the Intel Xeon Phi for Supervised Deep Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.DC","authors_text":"Andre Viebke, Sabri Pllana","submitted_at":"2015-06-30T12:54:09Z","abstract_excerpt":"Supervised learning of Convolutional Neural Networks (CNNs), also known as supervised Deep Learning, is a computationally demanding process. To find the most suitable parameters of a network for a given application, numerous training sessions are required. Therefore, reducing the training time per session is essential to fully utilize CNNs in practice. While numerous research groups have addressed the training of CNNs using GPUs, so far not much attention has been paid to the Intel Xeon Phi coprocessor. In this paper we investigate empirically and theoretically the potential of the Intel Xeon "},"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":"1506.09067","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.DC","submitted_at":"2015-06-30T12:54:09Z","cross_cats_sorted":[],"title_canon_sha256":"94358d9c15d27550a20f145214a1383c1e9e47ed0cefce0ff80dd5e472c9b806","abstract_canon_sha256":"b697098c1306ca3e0101a30a506151900b38659b7f08c2d9c56aa09f2d6b74e0"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:37:33.729626Z","signature_b64":"T691bIBHp9FgBhOLSSbJY5JIoms+pfwP9+CaOWR3ARduumvT5dJRvIBU5UunD2rBABw/m1EM+Hu20UXW94hIDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0ed0f067bbab4bbb9db30eb6ba1020162450de6393b8e6fd45434e69c5f71e68","last_reissued_at":"2026-05-18T01:37:33.728980Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:37:33.728980Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"The Potential of the Intel Xeon Phi for Supervised Deep Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.DC","authors_text":"Andre Viebke, Sabri Pllana","submitted_at":"2015-06-30T12:54:09Z","abstract_excerpt":"Supervised learning of Convolutional Neural Networks (CNNs), also known as supervised Deep Learning, is a computationally demanding process. To find the most suitable parameters of a network for a given application, numerous training sessions are required. Therefore, reducing the training time per session is essential to fully utilize CNNs in practice. While numerous research groups have addressed the training of CNNs using GPUs, so far not much attention has been paid to the Intel Xeon Phi coprocessor. In this paper we investigate empirically and theoretically the potential of the Intel Xeon "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1506.09067","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":"1506.09067","created_at":"2026-05-18T01:37:33.729093+00:00"},{"alias_kind":"arxiv_version","alias_value":"1506.09067v1","created_at":"2026-05-18T01:37:33.729093+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1506.09067","created_at":"2026-05-18T01:37:33.729093+00:00"},{"alias_kind":"pith_short_12","alias_value":"B3IPAZ53VNF3","created_at":"2026-05-18T12:29:14.074870+00:00"},{"alias_kind":"pith_short_16","alias_value":"B3IPAZ53VNF3XHNT","created_at":"2026-05-18T12:29:14.074870+00:00"},{"alias_kind":"pith_short_8","alias_value":"B3IPAZ53","created_at":"2026-05-18T12:29:14.074870+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/B3IPAZ53VNF3XHNTB23LUEBACY","json":"https://pith.science/pith/B3IPAZ53VNF3XHNTB23LUEBACY.json","graph_json":"https://pith.science/api/pith-number/B3IPAZ53VNF3XHNTB23LUEBACY/graph.json","events_json":"https://pith.science/api/pith-number/B3IPAZ53VNF3XHNTB23LUEBACY/events.json","paper":"https://pith.science/paper/B3IPAZ53"},"agent_actions":{"view_html":"https://pith.science/pith/B3IPAZ53VNF3XHNTB23LUEBACY","download_json":"https://pith.science/pith/B3IPAZ53VNF3XHNTB23LUEBACY.json","view_paper":"https://pith.science/paper/B3IPAZ53","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1506.09067&json=true","fetch_graph":"https://pith.science/api/pith-number/B3IPAZ53VNF3XHNTB23LUEBACY/graph.json","fetch_events":"https://pith.science/api/pith-number/B3IPAZ53VNF3XHNTB23LUEBACY/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/B3IPAZ53VNF3XHNTB23LUEBACY/action/timestamp_anchor","attest_storage":"https://pith.science/pith/B3IPAZ53VNF3XHNTB23LUEBACY/action/storage_attestation","attest_author":"https://pith.science/pith/B3IPAZ53VNF3XHNTB23LUEBACY/action/author_attestation","sign_citation":"https://pith.science/pith/B3IPAZ53VNF3XHNTB23LUEBACY/action/citation_signature","submit_replication":"https://pith.science/pith/B3IPAZ53VNF3XHNTB23LUEBACY/action/replication_record"}},"created_at":"2026-05-18T01:37:33.729093+00:00","updated_at":"2026-05-18T01:37:33.729093+00:00"}