{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2020:CX3H3D2FOPKTCXO4NCBK6SWBYK","short_pith_number":"pith:CX3H3D2F","canonical_record":{"source":{"id":"2003.08732","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-03-11T18:16:51Z","cross_cats_sorted":["cs.CV","eess.IV"],"title_canon_sha256":"1edc5e4f22c53578ff21b921dd85d5c7cf8ef32d864c789821f6029dd60653c8","abstract_canon_sha256":"e5d83fe057709205611e8e027b2fee4d55a5dcb6d3c1cccc05c0f205c120ba74"},"schema_version":"1.0"},"canonical_sha256":"15f67d8f4573d5315ddc6882af4ac1c2b1267d54dd56b545feb1c4cb6e496d45","source":{"kind":"arxiv","id":"2003.08732","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2003.08732","created_at":"2026-07-05T00:49:13Z"},{"alias_kind":"arxiv_version","alias_value":"2003.08732v1","created_at":"2026-07-05T00:49:13Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2003.08732","created_at":"2026-07-05T00:49:13Z"},{"alias_kind":"pith_short_12","alias_value":"CX3H3D2FOPKT","created_at":"2026-07-05T00:49:13Z"},{"alias_kind":"pith_short_16","alias_value":"CX3H3D2FOPKTCXO4","created_at":"2026-07-05T00:49:13Z"},{"alias_kind":"pith_short_8","alias_value":"CX3H3D2F","created_at":"2026-07-05T00:49:13Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2020:CX3H3D2FOPKTCXO4NCBK6SWBYK","target":"record","payload":{"canonical_record":{"source":{"id":"2003.08732","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-03-11T18:16:51Z","cross_cats_sorted":["cs.CV","eess.IV"],"title_canon_sha256":"1edc5e4f22c53578ff21b921dd85d5c7cf8ef32d864c789821f6029dd60653c8","abstract_canon_sha256":"e5d83fe057709205611e8e027b2fee4d55a5dcb6d3c1cccc05c0f205c120ba74"},"schema_version":"1.0"},"canonical_sha256":"15f67d8f4573d5315ddc6882af4ac1c2b1267d54dd56b545feb1c4cb6e496d45","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:49:13.555790Z","signature_b64":"ZmiHF+ye+OmwDY/Ccc1oi1g6J1MPVaBNj4EMlzYYjwYXKtFeZ5XRK5gifAgOFaJqmIbyNeJcPYBDENoPdF+gAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"15f67d8f4573d5315ddc6882af4ac1c2b1267d54dd56b545feb1c4cb6e496d45","last_reissued_at":"2026-07-05T00:49:13.555393Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:49:13.555393Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2003.08732","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T00:49:13Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"nV4k1TT4WgwVf4yTCOIb2j/OFcDYGlr7cd8L+Mc/APCgIoRp5bOvX2FULEJ8r5huneno05N0uFCIuF0wCZSYCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-06T13:48:32.131568Z"},"content_sha256":"3f05486d86f1c873e92f7950ef4b208432d07e6cfcd3a13264ca532fbeb879ed","schema_version":"1.0","event_id":"sha256:3f05486d86f1c873e92f7950ef4b208432d07e6cfcd3a13264ca532fbeb879ed"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2020:CX3H3D2FOPKTCXO4NCBK6SWBYK","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Addressing the Memory Bottleneck in AI Model Training","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV","eess.IV"],"primary_cat":"cs.LG","authors_text":"Bhavesh Patel, Chad Martin, David Ojika, G. Anthony Reina, Prashant Shah, Trent Boyer","submitted_at":"2020-03-11T18:16:51Z","abstract_excerpt":"Using medical imaging as case-study, we demonstrate how Intel-optimized TensorFlow on an x86-based server equipped with 2nd Generation Intel Xeon Scalable Processors with large system memory allows for the training of memory-intensive AI/deep-learning models in a scale-up server configuration. We believe our work represents the first training of a deep neural network having large memory footprint (~ 1 TB) on a single-node server. We recommend this configuration to scientists and researchers who wish to develop large, state-of-the-art AI models but are currently limited by memory."},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2003.08732","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2003.08732/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T00:49:13Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"hDrj2h2MJFaYdCuFjmW42xezJWPOEC5KBMv1/RfcskdmF5qBC9R3MlI+3ershIYhCaekSIb2ZP90veq1F2tjBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-06T13:48:32.131938Z"},"content_sha256":"de0603e9431b10d8ec942f507d6f362a26c93a2112aaf63f8ebd110c35460aa4","schema_version":"1.0","event_id":"sha256:de0603e9431b10d8ec942f507d6f362a26c93a2112aaf63f8ebd110c35460aa4"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/CX3H3D2FOPKTCXO4NCBK6SWBYK/bundle.json","state_url":"https://pith.science/pith/CX3H3D2FOPKTCXO4NCBK6SWBYK/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/CX3H3D2FOPKTCXO4NCBK6SWBYK/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-07-06T13:48:32Z","links":{"resolver":"https://pith.science/pith/CX3H3D2FOPKTCXO4NCBK6SWBYK","bundle":"https://pith.science/pith/CX3H3D2FOPKTCXO4NCBK6SWBYK/bundle.json","state":"https://pith.science/pith/CX3H3D2FOPKTCXO4NCBK6SWBYK/state.json","well_known_bundle":"https://pith.science/.well-known/pith/CX3H3D2FOPKTCXO4NCBK6SWBYK/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2020:CX3H3D2FOPKTCXO4NCBK6SWBYK","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"e5d83fe057709205611e8e027b2fee4d55a5dcb6d3c1cccc05c0f205c120ba74","cross_cats_sorted":["cs.CV","eess.IV"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-03-11T18:16:51Z","title_canon_sha256":"1edc5e4f22c53578ff21b921dd85d5c7cf8ef32d864c789821f6029dd60653c8"},"schema_version":"1.0","source":{"id":"2003.08732","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2003.08732","created_at":"2026-07-05T00:49:13Z"},{"alias_kind":"arxiv_version","alias_value":"2003.08732v1","created_at":"2026-07-05T00:49:13Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2003.08732","created_at":"2026-07-05T00:49:13Z"},{"alias_kind":"pith_short_12","alias_value":"CX3H3D2FOPKT","created_at":"2026-07-05T00:49:13Z"},{"alias_kind":"pith_short_16","alias_value":"CX3H3D2FOPKTCXO4","created_at":"2026-07-05T00:49:13Z"},{"alias_kind":"pith_short_8","alias_value":"CX3H3D2F","created_at":"2026-07-05T00:49:13Z"}],"graph_snapshots":[{"event_id":"sha256:de0603e9431b10d8ec942f507d6f362a26c93a2112aaf63f8ebd110c35460aa4","target":"graph","created_at":"2026-07-05T00:49:13Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2003.08732/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Using medical imaging as case-study, we demonstrate how Intel-optimized TensorFlow on an x86-based server equipped with 2nd Generation Intel Xeon Scalable Processors with large system memory allows for the training of memory-intensive AI/deep-learning models in a scale-up server configuration. We believe our work represents the first training of a deep neural network having large memory footprint (~ 1 TB) on a single-node server. We recommend this configuration to scientists and researchers who wish to develop large, state-of-the-art AI models but are currently limited by memory.","authors_text":"Bhavesh Patel, Chad Martin, David Ojika, G. Anthony Reina, Prashant Shah, Trent Boyer","cross_cats":["cs.CV","eess.IV"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-03-11T18:16:51Z","title":"Addressing the Memory Bottleneck in AI Model Training"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2003.08732","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:3f05486d86f1c873e92f7950ef4b208432d07e6cfcd3a13264ca532fbeb879ed","target":"record","created_at":"2026-07-05T00:49:13Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"e5d83fe057709205611e8e027b2fee4d55a5dcb6d3c1cccc05c0f205c120ba74","cross_cats_sorted":["cs.CV","eess.IV"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-03-11T18:16:51Z","title_canon_sha256":"1edc5e4f22c53578ff21b921dd85d5c7cf8ef32d864c789821f6029dd60653c8"},"schema_version":"1.0","source":{"id":"2003.08732","kind":"arxiv","version":1}},"canonical_sha256":"15f67d8f4573d5315ddc6882af4ac1c2b1267d54dd56b545feb1c4cb6e496d45","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"15f67d8f4573d5315ddc6882af4ac1c2b1267d54dd56b545feb1c4cb6e496d45","first_computed_at":"2026-07-05T00:49:13.555393Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T00:49:13.555393Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"ZmiHF+ye+OmwDY/Ccc1oi1g6J1MPVaBNj4EMlzYYjwYXKtFeZ5XRK5gifAgOFaJqmIbyNeJcPYBDENoPdF+gAg==","signature_status":"signed_v1","signed_at":"2026-07-05T00:49:13.555790Z","signed_message":"canonical_sha256_bytes"},"source_id":"2003.08732","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:3f05486d86f1c873e92f7950ef4b208432d07e6cfcd3a13264ca532fbeb879ed","sha256:de0603e9431b10d8ec942f507d6f362a26c93a2112aaf63f8ebd110c35460aa4"],"state_sha256":"443e851f8ecb42850fd2e8b3881e7a6e19e776550cee8ff908fc8307f4d67e99"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"tEfZCo+en9sD0KGVe1EyTeEhX6DekzuQbelYwFixK9KRqE7dg7w/abBy15IiMo3113mfU/M6KpY7WrKjsOY1DA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-06T13:48:32.133846Z","bundle_sha256":"2df8bc4bd8796b91876cf15eec6d95a66f17f3303b019087c297d3e5e121898f"}}