{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2016:IWKG747RTQ7QOSRUB2MUQDLG56","short_pith_number":"pith:IWKG747R","schema_version":"1.0","canonical_sha256":"45946ff3f19c3f074a340e99480d66ef897b5f2aa07386ca7732fca4f70d5806","source":{"kind":"arxiv","id":"1611.06945","version":1},"attestation_state":"computed","paper":{"title":"A Metaprogramming and Autotuning Framework for Deploying Deep Learning Applications","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.DC","cs.MS"],"primary_cat":"cs.NE","authors_text":"Ali Jannesari, Kurt Keutzer, Matthew W. Moskewicz","submitted_at":"2016-11-21T18:49:23Z","abstract_excerpt":"In recent years, deep neural networks (DNNs), have yielded strong results on a wide range of applications. Graphics Processing Units (GPUs) have been one key enabling factor leading to the current popularity of DNNs. However, despite increasing hardware flexibility and software programming toolchain maturity, high efficiency GPU programming remains difficult: it suffers from high complexity, low productivity, and low portability. GPU vendors such as NVIDIA have spent enormous effort to write special-purpose DNN libraries. However, on other hardware targets, especially mobile GPUs, such vendor "},"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":"1611.06945","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NE","submitted_at":"2016-11-21T18:49:23Z","cross_cats_sorted":["cs.DC","cs.MS"],"title_canon_sha256":"d8f7057843df4b28619f2b714b51c64383167238a1af0572c03a6764cf21fa9b","abstract_canon_sha256":"4fc952efded56a01a7a43be9b7930738d43f4d0d3b9e1663ac3474354ea66c5f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:57:32.459766Z","signature_b64":"bmVWDHBK6zpm3Lwr2h5WrrTDiqQ6eE9kJnRGvkGCJlGw1KCSo85fwZJ41IrKmRSFdy9ff0tPDRIaXechZUegAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"45946ff3f19c3f074a340e99480d66ef897b5f2aa07386ca7732fca4f70d5806","last_reissued_at":"2026-05-18T00:57:32.459304Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:57:32.459304Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Metaprogramming and Autotuning Framework for Deploying Deep Learning Applications","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.DC","cs.MS"],"primary_cat":"cs.NE","authors_text":"Ali Jannesari, Kurt Keutzer, Matthew W. Moskewicz","submitted_at":"2016-11-21T18:49:23Z","abstract_excerpt":"In recent years, deep neural networks (DNNs), have yielded strong results on a wide range of applications. Graphics Processing Units (GPUs) have been one key enabling factor leading to the current popularity of DNNs. However, despite increasing hardware flexibility and software programming toolchain maturity, high efficiency GPU programming remains difficult: it suffers from high complexity, low productivity, and low portability. GPU vendors such as NVIDIA have spent enormous effort to write special-purpose DNN libraries. However, on other hardware targets, especially mobile GPUs, such vendor "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1611.06945","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":"1611.06945","created_at":"2026-05-18T00:57:32.459390+00:00"},{"alias_kind":"arxiv_version","alias_value":"1611.06945v1","created_at":"2026-05-18T00:57:32.459390+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1611.06945","created_at":"2026-05-18T00:57:32.459390+00:00"},{"alias_kind":"pith_short_12","alias_value":"IWKG747RTQ7Q","created_at":"2026-05-18T12:30:22.444734+00:00"},{"alias_kind":"pith_short_16","alias_value":"IWKG747RTQ7QOSRU","created_at":"2026-05-18T12:30:22.444734+00:00"},{"alias_kind":"pith_short_8","alias_value":"IWKG747R","created_at":"2026-05-18T12:30:22.444734+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/IWKG747RTQ7QOSRUB2MUQDLG56","json":"https://pith.science/pith/IWKG747RTQ7QOSRUB2MUQDLG56.json","graph_json":"https://pith.science/api/pith-number/IWKG747RTQ7QOSRUB2MUQDLG56/graph.json","events_json":"https://pith.science/api/pith-number/IWKG747RTQ7QOSRUB2MUQDLG56/events.json","paper":"https://pith.science/paper/IWKG747R"},"agent_actions":{"view_html":"https://pith.science/pith/IWKG747RTQ7QOSRUB2MUQDLG56","download_json":"https://pith.science/pith/IWKG747RTQ7QOSRUB2MUQDLG56.json","view_paper":"https://pith.science/paper/IWKG747R","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1611.06945&json=true","fetch_graph":"https://pith.science/api/pith-number/IWKG747RTQ7QOSRUB2MUQDLG56/graph.json","fetch_events":"https://pith.science/api/pith-number/IWKG747RTQ7QOSRUB2MUQDLG56/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/IWKG747RTQ7QOSRUB2MUQDLG56/action/timestamp_anchor","attest_storage":"https://pith.science/pith/IWKG747RTQ7QOSRUB2MUQDLG56/action/storage_attestation","attest_author":"https://pith.science/pith/IWKG747RTQ7QOSRUB2MUQDLG56/action/author_attestation","sign_citation":"https://pith.science/pith/IWKG747RTQ7QOSRUB2MUQDLG56/action/citation_signature","submit_replication":"https://pith.science/pith/IWKG747RTQ7QOSRUB2MUQDLG56/action/replication_record"}},"created_at":"2026-05-18T00:57:32.459390+00:00","updated_at":"2026-05-18T00:57:32.459390+00:00"}