{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2021:AALR2P54MOPPXI2SADJSUAMRFF","short_pith_number":"pith:AALR2P54","canonical_record":{"source":{"id":"2101.07344","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2021-01-18T22:13:08Z","cross_cats_sorted":["cs.DC","cs.PF"],"title_canon_sha256":"f7350e3d7d90f99ef78e26a6e97f9f62a979e0b129d6bf80cc93049a7d4a1dc2","abstract_canon_sha256":"0b5f1cae191ebe1bbedf16a75ea001ebc0291f9ccb3cc2a60034e4775421a80b"},"schema_version":"1.0"},"canonical_sha256":"00171d3fbc639efba35200d32a0191294bd466e242b03433e588fe7e970f17f7","source":{"kind":"arxiv","id":"2101.07344","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2101.07344","created_at":"2026-07-05T02:07:57Z"},{"alias_kind":"arxiv_version","alias_value":"2101.07344v1","created_at":"2026-07-05T02:07:57Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2101.07344","created_at":"2026-07-05T02:07:57Z"},{"alias_kind":"pith_short_12","alias_value":"AALR2P54MOPP","created_at":"2026-07-05T02:07:57Z"},{"alias_kind":"pith_short_16","alias_value":"AALR2P54MOPPXI2S","created_at":"2026-07-05T02:07:57Z"},{"alias_kind":"pith_short_8","alias_value":"AALR2P54","created_at":"2026-07-05T02:07:57Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2021:AALR2P54MOPPXI2SADJSUAMRFF","target":"record","payload":{"canonical_record":{"source":{"id":"2101.07344","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2021-01-18T22:13:08Z","cross_cats_sorted":["cs.DC","cs.PF"],"title_canon_sha256":"f7350e3d7d90f99ef78e26a6e97f9f62a979e0b129d6bf80cc93049a7d4a1dc2","abstract_canon_sha256":"0b5f1cae191ebe1bbedf16a75ea001ebc0291f9ccb3cc2a60034e4775421a80b"},"schema_version":"1.0"},"canonical_sha256":"00171d3fbc639efba35200d32a0191294bd466e242b03433e588fe7e970f17f7","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:07:57.006124Z","signature_b64":"zD/AydguWvT6IROxeuCJL8wIQH1yJZ4VgxGo9OeZ1EkBD/ZEXBVn/fIwJcrZdMiuk4hprzGsCn6mIgk8wMy2BQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"00171d3fbc639efba35200d32a0191294bd466e242b03433e588fe7e970f17f7","last_reissued_at":"2026-07-05T02:07:57.005772Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:07:57.005772Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2101.07344","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":"2026-07-05T02:07:57Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"rJ2pG3s2S2iJ4If6nYLG+2vcZ1t0cI7/0CyjJv+fvkK+CKnHCQxHqjWVpTNhnfFM0jSD2u6i3jlS4+wTeZ+rDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-27T04:29:57.099863Z"},"content_sha256":"94b8c7ee4b9de3260643a002201c1f9431221dee613c63c2a37a53623649b962","schema_version":"1.0","event_id":"sha256:94b8c7ee4b9de3260643a002201c1f9431221dee613c63c2a37a53623649b962"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2021:AALR2P54MOPPXI2SADJSUAMRFF","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Accelerating Deep Learning Inference via Learned Caches","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.DC","cs.PF"],"primary_cat":"cs.LG","authors_text":"Adarsh Kumar, Aditya Akella, Arjun Balasubramanian, Han Cao, Shivaram Venkataraman, Yuhan Liu","submitted_at":"2021-01-18T22:13:08Z","abstract_excerpt":"Deep Neural Networks (DNNs) are witnessing increased adoption in multiple domains owing to their high accuracy in solving real-world problems. However, this high accuracy has been achieved by building deeper networks, posing a fundamental challenge to the low latency inference desired by user-facing applications. Current low latency solutions trade-off on accuracy or fail to exploit the inherent temporal locality in prediction serving workloads.\n  We observe that caching hidden layer outputs of the DNN can introduce a form of late-binding where inference requests only consume the amount of com"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2101.07344","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/2101.07344/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":"2026-07-05T02:07:57Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"5WwsqHc2DwJk+sJCJNh/l5+6EBnOQiWJeX02S7Zs0yg/60VM4TFb3cvVWkaIZgiNGgLbENnOg7BFXPolYOySCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-27T04:29:57.100227Z"},"content_sha256":"ec5e601302d19a05095eb37bd65cdc2f8befe465e1f600b97c57d0cb4a032abe","schema_version":"1.0","event_id":"sha256:ec5e601302d19a05095eb37bd65cdc2f8befe465e1f600b97c57d0cb4a032abe"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/AALR2P54MOPPXI2SADJSUAMRFF/bundle.json","state_url":"https://pith.science/pith/AALR2P54MOPPXI2SADJSUAMRFF/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/AALR2P54MOPPXI2SADJSUAMRFF/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":"2026-07-27T04:29:57Z","links":{"resolver":"https://pith.science/pith/AALR2P54MOPPXI2SADJSUAMRFF","bundle":"https://pith.science/pith/AALR2P54MOPPXI2SADJSUAMRFF/bundle.json","state":"https://pith.science/pith/AALR2P54MOPPXI2SADJSUAMRFF/state.json","well_known_bundle":"https://pith.science/.well-known/pith/AALR2P54MOPPXI2SADJSUAMRFF/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2021:AALR2P54MOPPXI2SADJSUAMRFF","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":"0b5f1cae191ebe1bbedf16a75ea001ebc0291f9ccb3cc2a60034e4775421a80b","cross_cats_sorted":["cs.DC","cs.PF"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2021-01-18T22:13:08Z","title_canon_sha256":"f7350e3d7d90f99ef78e26a6e97f9f62a979e0b129d6bf80cc93049a7d4a1dc2"},"schema_version":"1.0","source":{"id":"2101.07344","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2101.07344","created_at":"2026-07-05T02:07:57Z"},{"alias_kind":"arxiv_version","alias_value":"2101.07344v1","created_at":"2026-07-05T02:07:57Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2101.07344","created_at":"2026-07-05T02:07:57Z"},{"alias_kind":"pith_short_12","alias_value":"AALR2P54MOPP","created_at":"2026-07-05T02:07:57Z"},{"alias_kind":"pith_short_16","alias_value":"AALR2P54MOPPXI2S","created_at":"2026-07-05T02:07:57Z"},{"alias_kind":"pith_short_8","alias_value":"AALR2P54","created_at":"2026-07-05T02:07:57Z"}],"graph_snapshots":[{"event_id":"sha256:ec5e601302d19a05095eb37bd65cdc2f8befe465e1f600b97c57d0cb4a032abe","target":"graph","created_at":"2026-07-05T02:07:57Z","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/2101.07344/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Deep Neural Networks (DNNs) are witnessing increased adoption in multiple domains owing to their high accuracy in solving real-world problems. However, this high accuracy has been achieved by building deeper networks, posing a fundamental challenge to the low latency inference desired by user-facing applications. Current low latency solutions trade-off on accuracy or fail to exploit the inherent temporal locality in prediction serving workloads.\n  We observe that caching hidden layer outputs of the DNN can introduce a form of late-binding where inference requests only consume the amount of com","authors_text":"Adarsh Kumar, Aditya Akella, Arjun Balasubramanian, Han Cao, Shivaram Venkataraman, Yuhan Liu","cross_cats":["cs.DC","cs.PF"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2021-01-18T22:13:08Z","title":"Accelerating Deep Learning Inference via Learned Caches"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2101.07344","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":"sha256:94b8c7ee4b9de3260643a002201c1f9431221dee613c63c2a37a53623649b962","target":"record","created_at":"2026-07-05T02:07:57Z","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":"0b5f1cae191ebe1bbedf16a75ea001ebc0291f9ccb3cc2a60034e4775421a80b","cross_cats_sorted":["cs.DC","cs.PF"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2021-01-18T22:13:08Z","title_canon_sha256":"f7350e3d7d90f99ef78e26a6e97f9f62a979e0b129d6bf80cc93049a7d4a1dc2"},"schema_version":"1.0","source":{"id":"2101.07344","kind":"arxiv","version":1}},"canonical_sha256":"00171d3fbc639efba35200d32a0191294bd466e242b03433e588fe7e970f17f7","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"00171d3fbc639efba35200d32a0191294bd466e242b03433e588fe7e970f17f7","first_computed_at":"2026-07-05T02:07:57.005772Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T02:07:57.005772Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"zD/AydguWvT6IROxeuCJL8wIQH1yJZ4VgxGo9OeZ1EkBD/ZEXBVn/fIwJcrZdMiuk4hprzGsCn6mIgk8wMy2BQ==","signature_status":"signed_v1","signed_at":"2026-07-05T02:07:57.006124Z","signed_message":"canonical_sha256_bytes"},"source_id":"2101.07344","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:94b8c7ee4b9de3260643a002201c1f9431221dee613c63c2a37a53623649b962","sha256:ec5e601302d19a05095eb37bd65cdc2f8befe465e1f600b97c57d0cb4a032abe"],"state_sha256":"4d649b24e8736474839a04ef1cecf2ea161eb63ab369019db99565eb5b4fa3cb"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"XqHDrFpwiQUbyeArV06sZPvOcYUVTWnQk4SHM982LsUqjwteDU7lC8VB+uinnIchrxvLPjyYcgf4FTdC9ObOBQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-27T04:29:57.102789Z","bundle_sha256":"bd7c4072e1d929a0955724f560cdbb60c0bd86fbac5c24916fbbed98475e6ec6"}}