{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:6ISI6BNWF24OLWH5GSVL6KZ3IM","short_pith_number":"pith:6ISI6BNW","canonical_record":{"source":{"id":"1907.10701","kind":"arxiv","version":4},"metadata":{"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2019-07-24T20:18:28Z","cross_cats_sorted":["cs.PF","stat.ML"],"title_canon_sha256":"0ea3690756adea8194792dfa69db08268dc291f16724c98eef9b8c306908e564","abstract_canon_sha256":"298c27ea4299f879e8d7631615e7937dc98ab51f58b146d7c9ea269897cab6e5"},"schema_version":"1.0"},"canonical_sha256":"f2248f05b62eb8e5d8fd34aabf2b3b4323ddd19cd0181fe5038e4ef8e6f4a689","source":{"kind":"arxiv","id":"1907.10701","version":4},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1907.10701","created_at":"2026-07-05T00:13:51Z"},{"alias_kind":"arxiv_version","alias_value":"1907.10701v4","created_at":"2026-07-05T00:13:51Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1907.10701","created_at":"2026-07-05T00:13:51Z"},{"alias_kind":"pith_short_12","alias_value":"6ISI6BNWF24O","created_at":"2026-07-05T00:13:51Z"},{"alias_kind":"pith_short_16","alias_value":"6ISI6BNWF24OLWH5","created_at":"2026-07-05T00:13:51Z"},{"alias_kind":"pith_short_8","alias_value":"6ISI6BNW","created_at":"2026-07-05T00:13:51Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:6ISI6BNWF24OLWH5GSVL6KZ3IM","target":"record","payload":{"canonical_record":{"source":{"id":"1907.10701","kind":"arxiv","version":4},"metadata":{"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2019-07-24T20:18:28Z","cross_cats_sorted":["cs.PF","stat.ML"],"title_canon_sha256":"0ea3690756adea8194792dfa69db08268dc291f16724c98eef9b8c306908e564","abstract_canon_sha256":"298c27ea4299f879e8d7631615e7937dc98ab51f58b146d7c9ea269897cab6e5"},"schema_version":"1.0"},"canonical_sha256":"f2248f05b62eb8e5d8fd34aabf2b3b4323ddd19cd0181fe5038e4ef8e6f4a689","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:13:51.864377Z","signature_b64":"DA/H9VB1Mf8gzyb/fXSGGFpobZ4GjxVkafCGYAp7cDMjKikWaVzfnmtw+09GEpdbv8xqfBw46ZyY+jHejmO/Dg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f2248f05b62eb8e5d8fd34aabf2b3b4323ddd19cd0181fe5038e4ef8e6f4a689","last_reissued_at":"2026-07-05T00:13:51.864027Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:13:51.864027Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1907.10701","source_version":4,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T00:13:51Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"zj2Y6IteCeJxM7Nq0OImEoqu+ReHiXub+SvbWJUsjojZ50vwluGO/39bpIuFGmFVsIjhcF5uDZRgF3z/xWhYDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-06T18:32:13.321823Z"},"content_sha256":"5563c402de1540e5911b075ff9956ae94a72628d89d745f475bcb9e0b34c0a82","schema_version":"1.0","event_id":"sha256:5563c402de1540e5911b075ff9956ae94a72628d89d745f475bcb9e0b34c0a82"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:6ISI6BNWF24OLWH5GSVL6KZ3IM","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Benchmarking TPU, GPU, and CPU Platforms for Deep Learning","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":["cs.PF","stat.ML"],"primary_cat":"cs.LG","authors_text":"David Brooks, Gu-Yeon Wei, Yu Emma Wang","submitted_at":"2019-07-24T20:18:28Z","abstract_excerpt":"Training deep learning models is compute-intensive and there is an industry-wide trend towards hardware specialization to improve performance. To systematically benchmark deep learning platforms, we introduce ParaDnn, a parameterized benchmark suite for deep learning that generates end-to-end models for fully connected (FC), convolutional (CNN), and recurrent (RNN) neural networks. Along with six real-world models, we benchmark Google's Cloud TPU v2/v3, NVIDIA's V100 GPU, and an Intel Skylake CPU platform. We take a deep dive into TPU architecture, reveal its bottlenecks, and highlight valuabl"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1907.10701","kind":"arxiv","version":4},"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/1907.10701/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":"2026-07-05T00:13:51Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"0OwhhJPGjOxQ1Q9KAiUe9So7VDl+S7pwq9AC3393bwWXyw7j3ZPLvpJiwEVyfZvTiZzH+NhPDj65+LS8phFqBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-06T18:32:13.322159Z"},"content_sha256":"4b7dab2c5dd17c22511b5a0151c0cc2158bf5ca086496a47a8bd1abf39c3d082","schema_version":"1.0","event_id":"sha256:4b7dab2c5dd17c22511b5a0151c0cc2158bf5ca086496a47a8bd1abf39c3d082"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/6ISI6BNWF24OLWH5GSVL6KZ3IM/bundle.json","state_url":"https://pith.science/pith/6ISI6BNWF24OLWH5GSVL6KZ3IM/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/6ISI6BNWF24OLWH5GSVL6KZ3IM/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":"2026-08-06T18:32:13Z","links":{"resolver":"https://pith.science/pith/6ISI6BNWF24OLWH5GSVL6KZ3IM","bundle":"https://pith.science/pith/6ISI6BNWF24OLWH5GSVL6KZ3IM/bundle.json","state":"https://pith.science/pith/6ISI6BNWF24OLWH5GSVL6KZ3IM/state.json","well_known_bundle":"https://pith.science/.well-known/pith/6ISI6BNWF24OLWH5GSVL6KZ3IM/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:6ISI6BNWF24OLWH5GSVL6KZ3IM","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":"298c27ea4299f879e8d7631615e7937dc98ab51f58b146d7c9ea269897cab6e5","cross_cats_sorted":["cs.PF","stat.ML"],"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2019-07-24T20:18:28Z","title_canon_sha256":"0ea3690756adea8194792dfa69db08268dc291f16724c98eef9b8c306908e564"},"schema_version":"1.0","source":{"id":"1907.10701","kind":"arxiv","version":4}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1907.10701","created_at":"2026-07-05T00:13:51Z"},{"alias_kind":"arxiv_version","alias_value":"1907.10701v4","created_at":"2026-07-05T00:13:51Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1907.10701","created_at":"2026-07-05T00:13:51Z"},{"alias_kind":"pith_short_12","alias_value":"6ISI6BNWF24O","created_at":"2026-07-05T00:13:51Z"},{"alias_kind":"pith_short_16","alias_value":"6ISI6BNWF24OLWH5","created_at":"2026-07-05T00:13:51Z"},{"alias_kind":"pith_short_8","alias_value":"6ISI6BNW","created_at":"2026-07-05T00:13:51Z"}],"graph_snapshots":[{"event_id":"sha256:4b7dab2c5dd17c22511b5a0151c0cc2158bf5ca086496a47a8bd1abf39c3d082","target":"graph","created_at":"2026-07-05T00:13:51Z","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/1907.10701/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Training deep learning models is compute-intensive and there is an industry-wide trend towards hardware specialization to improve performance. To systematically benchmark deep learning platforms, we introduce ParaDnn, a parameterized benchmark suite for deep learning that generates end-to-end models for fully connected (FC), convolutional (CNN), and recurrent (RNN) neural networks. Along with six real-world models, we benchmark Google's Cloud TPU v2/v3, NVIDIA's V100 GPU, and an Intel Skylake CPU platform. We take a deep dive into TPU architecture, reveal its bottlenecks, and highlight valuabl","authors_text":"David Brooks, Gu-Yeon Wei, Yu Emma Wang","cross_cats":["cs.PF","stat.ML"],"headline":"","license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2019-07-24T20:18:28Z","title":"Benchmarking TPU, GPU, and CPU Platforms for Deep Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1907.10701","kind":"arxiv","version":4},"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":"sha256:5563c402de1540e5911b075ff9956ae94a72628d89d745f475bcb9e0b34c0a82","target":"record","created_at":"2026-07-05T00:13:51Z","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":"298c27ea4299f879e8d7631615e7937dc98ab51f58b146d7c9ea269897cab6e5","cross_cats_sorted":["cs.PF","stat.ML"],"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2019-07-24T20:18:28Z","title_canon_sha256":"0ea3690756adea8194792dfa69db08268dc291f16724c98eef9b8c306908e564"},"schema_version":"1.0","source":{"id":"1907.10701","kind":"arxiv","version":4}},"canonical_sha256":"f2248f05b62eb8e5d8fd34aabf2b3b4323ddd19cd0181fe5038e4ef8e6f4a689","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"f2248f05b62eb8e5d8fd34aabf2b3b4323ddd19cd0181fe5038e4ef8e6f4a689","first_computed_at":"2026-07-05T00:13:51.864027Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T00:13:51.864027Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"DA/H9VB1Mf8gzyb/fXSGGFpobZ4GjxVkafCGYAp7cDMjKikWaVzfnmtw+09GEpdbv8xqfBw46ZyY+jHejmO/Dg==","signature_status":"signed_v1","signed_at":"2026-07-05T00:13:51.864377Z","signed_message":"canonical_sha256_bytes"},"source_id":"1907.10701","source_kind":"arxiv","source_version":4}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:5563c402de1540e5911b075ff9956ae94a72628d89d745f475bcb9e0b34c0a82","sha256:4b7dab2c5dd17c22511b5a0151c0cc2158bf5ca086496a47a8bd1abf39c3d082"],"state_sha256":"e2e2d8f343e04fd1ddf0041425b7dc2f0433c5b1200d51df842681956da3a55d"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"+SK8mk91hgA100yzN+tYpIUuLgVQkN3fVxkQbslAbgtUooZdw4I6Mol97PBL6PoHhmX0z3YKjZ+SYxJdSIX+DQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-06T18:32:13.325466Z","bundle_sha256":"be750af6e50c5beb755a9a31eacfcb43188acb660ab7695655555cc77a4f8d5c"}}