{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:HW7F4KDFQKEWILJM5VQRR6P2F3","short_pith_number":"pith:HW7F4KDF","canonical_record":{"source":{"id":"1908.06649","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.DS","submitted_at":"2019-08-19T08:59:46Z","cross_cats_sorted":["cs.AR","cs.DC","cs.LG"],"title_canon_sha256":"73ee5a7316637e059f90e1ee2552a1bff5e09a0dcd843958971079afe852b4a6","abstract_canon_sha256":"9d647a37bf0f6c990af39ecd016a68a2dffa343e9e30b80842e4741b237c8ffe"},"schema_version":"1.0"},"canonical_sha256":"3dbe5e28658289642d2ced6118f9fa2ecba4240533c9131aea4df844e0a4a6de","source":{"kind":"arxiv","id":"1908.06649","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1908.06649","created_at":"2026-07-05T01:17:24Z"},{"alias_kind":"arxiv_version","alias_value":"1908.06649v2","created_at":"2026-07-05T01:17:24Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1908.06649","created_at":"2026-07-05T01:17:24Z"},{"alias_kind":"pith_short_12","alias_value":"HW7F4KDFQKEW","created_at":"2026-07-05T01:17:24Z"},{"alias_kind":"pith_short_16","alias_value":"HW7F4KDFQKEWILJM","created_at":"2026-07-05T01:17:24Z"},{"alias_kind":"pith_short_8","alias_value":"HW7F4KDF","created_at":"2026-07-05T01:17:24Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:HW7F4KDFQKEWILJM5VQRR6P2F3","target":"record","payload":{"canonical_record":{"source":{"id":"1908.06649","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.DS","submitted_at":"2019-08-19T08:59:46Z","cross_cats_sorted":["cs.AR","cs.DC","cs.LG"],"title_canon_sha256":"73ee5a7316637e059f90e1ee2552a1bff5e09a0dcd843958971079afe852b4a6","abstract_canon_sha256":"9d647a37bf0f6c990af39ecd016a68a2dffa343e9e30b80842e4741b237c8ffe"},"schema_version":"1.0"},"canonical_sha256":"3dbe5e28658289642d2ced6118f9fa2ecba4240533c9131aea4df844e0a4a6de","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:17:24.724090Z","signature_b64":"ESNKolwgva7YUpv4DecrI/qk++6ACvyV8dGU20PdqW8ZMmAny3RJhumZt6PhG6jalTadwoLOpaZTjvAczbi1Ag==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3dbe5e28658289642d2ced6118f9fa2ecba4240533c9131aea4df844e0a4a6de","last_reissued_at":"2026-07-05T01:17:24.723611Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:17:24.723611Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1908.06649","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T01:17:24Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"oTk+JwLYxAq2ceZ1T15+lZ8AlKYPb/7/PVeBzu/wC7UN5eTBzkHVuloZS7ouGnDAf1V3dR0nhmP6zovDoQTWAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-17T01:02:13.006005Z"},"content_sha256":"30d18892c1a00816db1747488ea3c862fc7d3cd2bb48a17dd9170faae1c9aa3b","schema_version":"1.0","event_id":"sha256:30d18892c1a00816db1747488ea3c862fc7d3cd2bb48a17dd9170faae1c9aa3b"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:HW7F4KDFQKEWILJM5VQRR6P2F3","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"A Computational Model for Tensor Core Units","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AR","cs.DC","cs.LG"],"primary_cat":"cs.DS","authors_text":"Flavio Vella, Francesco Silvestri, Rezaul Chowdhury","submitted_at":"2019-08-19T08:59:46Z","abstract_excerpt":"To respond to the need of efficient training and inference of deep neural networks, a plethora of domain-specific hardware architectures have been introduced, such as Google Tensor Processing Units and NVIDIA Tensor Cores. A common feature of these architectures is a hardware circuit for efficiently computing a dense matrix multiplication of a given small size. In order to broaden the class of algorithms that exploit these systems, we propose a computational model, named the TCU model, that captures the ability to natively multiply small matrices. We then use the TCU model for designing fast a"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1908.06649","kind":"arxiv","version":2},"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/1908.06649/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":"2026-07-05T01:17:24Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"xJVAyseSfOtw9lxEiSyNdODzH+dRKiowOFp2v6BvtiNA2bjj8wEyUPlDxsOpbdDUsWsrQenNOuTmZYVFrHuiCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-17T01:02:13.006899Z"},"content_sha256":"494ef99368bcd911e978a0d26d41e95bbbb770f6929f040b1e3baaf9c3083150","schema_version":"1.0","event_id":"sha256:494ef99368bcd911e978a0d26d41e95bbbb770f6929f040b1e3baaf9c3083150"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/HW7F4KDFQKEWILJM5VQRR6P2F3/bundle.json","state_url":"https://pith.science/pith/HW7F4KDFQKEWILJM5VQRR6P2F3/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/HW7F4KDFQKEWILJM5VQRR6P2F3/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":"2026-08-17T01:02:13Z","links":{"resolver":"https://pith.science/pith/HW7F4KDFQKEWILJM5VQRR6P2F3","bundle":"https://pith.science/pith/HW7F4KDFQKEWILJM5VQRR6P2F3/bundle.json","state":"https://pith.science/pith/HW7F4KDFQKEWILJM5VQRR6P2F3/state.json","well_known_bundle":"https://pith.science/.well-known/pith/HW7F4KDFQKEWILJM5VQRR6P2F3/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:HW7F4KDFQKEWILJM5VQRR6P2F3","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":"9d647a37bf0f6c990af39ecd016a68a2dffa343e9e30b80842e4741b237c8ffe","cross_cats_sorted":["cs.AR","cs.DC","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.DS","submitted_at":"2019-08-19T08:59:46Z","title_canon_sha256":"73ee5a7316637e059f90e1ee2552a1bff5e09a0dcd843958971079afe852b4a6"},"schema_version":"1.0","source":{"id":"1908.06649","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1908.06649","created_at":"2026-07-05T01:17:24Z"},{"alias_kind":"arxiv_version","alias_value":"1908.06649v2","created_at":"2026-07-05T01:17:24Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1908.06649","created_at":"2026-07-05T01:17:24Z"},{"alias_kind":"pith_short_12","alias_value":"HW7F4KDFQKEW","created_at":"2026-07-05T01:17:24Z"},{"alias_kind":"pith_short_16","alias_value":"HW7F4KDFQKEWILJM","created_at":"2026-07-05T01:17:24Z"},{"alias_kind":"pith_short_8","alias_value":"HW7F4KDF","created_at":"2026-07-05T01:17:24Z"}],"graph_snapshots":[{"event_id":"sha256:494ef99368bcd911e978a0d26d41e95bbbb770f6929f040b1e3baaf9c3083150","target":"graph","created_at":"2026-07-05T01:17:24Z","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/1908.06649/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"To respond to the need of efficient training and inference of deep neural networks, a plethora of domain-specific hardware architectures have been introduced, such as Google Tensor Processing Units and NVIDIA Tensor Cores. A common feature of these architectures is a hardware circuit for efficiently computing a dense matrix multiplication of a given small size. In order to broaden the class of algorithms that exploit these systems, we propose a computational model, named the TCU model, that captures the ability to natively multiply small matrices. We then use the TCU model for designing fast a","authors_text":"Flavio Vella, Francesco Silvestri, Rezaul Chowdhury","cross_cats":["cs.AR","cs.DC","cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.DS","submitted_at":"2019-08-19T08:59:46Z","title":"A Computational Model for Tensor Core Units"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1908.06649","kind":"arxiv","version":2},"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":"sha256:30d18892c1a00816db1747488ea3c862fc7d3cd2bb48a17dd9170faae1c9aa3b","target":"record","created_at":"2026-07-05T01:17:24Z","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":"9d647a37bf0f6c990af39ecd016a68a2dffa343e9e30b80842e4741b237c8ffe","cross_cats_sorted":["cs.AR","cs.DC","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.DS","submitted_at":"2019-08-19T08:59:46Z","title_canon_sha256":"73ee5a7316637e059f90e1ee2552a1bff5e09a0dcd843958971079afe852b4a6"},"schema_version":"1.0","source":{"id":"1908.06649","kind":"arxiv","version":2}},"canonical_sha256":"3dbe5e28658289642d2ced6118f9fa2ecba4240533c9131aea4df844e0a4a6de","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"3dbe5e28658289642d2ced6118f9fa2ecba4240533c9131aea4df844e0a4a6de","first_computed_at":"2026-07-05T01:17:24.723611Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T01:17:24.723611Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"ESNKolwgva7YUpv4DecrI/qk++6ACvyV8dGU20PdqW8ZMmAny3RJhumZt6PhG6jalTadwoLOpaZTjvAczbi1Ag==","signature_status":"signed_v1","signed_at":"2026-07-05T01:17:24.724090Z","signed_message":"canonical_sha256_bytes"},"source_id":"1908.06649","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:30d18892c1a00816db1747488ea3c862fc7d3cd2bb48a17dd9170faae1c9aa3b","sha256:494ef99368bcd911e978a0d26d41e95bbbb770f6929f040b1e3baaf9c3083150"],"state_sha256":"f2957a657a7338ad07367d0c8434034796ab8591aa67c2fc46bbb217e61b4d38"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"7VWvWhJfyE5QO/6jWTIAqTQVy5jeTt04rYxu6aE4ewsVafX2oqSf9O6pPUjhf2SBvopvhhCExbWqKCfYRAd1AA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-17T01:02:13.014714Z","bundle_sha256":"c132ef8ba55ea4829fd9e08ddbed881dc9106e79ddcf1e17e3e31cd9a37a2b71"}}