{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2020:JRLRSIEID5UUKISO6UJXKNC4HD","short_pith_number":"pith:JRLRSIEI","schema_version":"1.0","canonical_sha256":"4c571920881f6945224ef51375345c38d4ae2fa5299799c7aa370d14adbb60de","source":{"kind":"arxiv","id":"2008.01814","version":2},"attestation_state":"computed","paper":{"title":"A Case For Adaptive Deep Neural Networks in Edge Computing","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.DC","authors_text":"Blesson Varghese, Francis McNamee, Ivor Spence, Peter Kilpatrick, Schahram Dustadar, Weisong Shi","submitted_at":"2020-08-04T20:23:50Z","abstract_excerpt":"Edge computing offers an additional layer of compute infrastructure closer to the data source before raw data from privacy-sensitive and performance-critical applications is transferred to a cloud data center. Deep Neural Networks (DNNs) are one class of applications that are reported to benefit from collaboratively computing between the edge and the cloud. A DNN is partitioned such that specific layers of the DNN are deployed onto the edge and the cloud to meet performance and privacy objectives. However, there is limited understanding of: (a) whether and how evolving operational conditions ("},"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":"2008.01814","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.DC","submitted_at":"2020-08-04T20:23:50Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"7f0f7f47a0bafd4bcc8060e5de765042d2efbad2963d4a9ba8ce5428fd44ee95","abstract_canon_sha256":"c4b7a15f74feb78c72db274075c647eea1ebc71da76a30a50343f8c51d2c6b66"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:59:57.033883Z","signature_b64":"z4Gu7zt/Lnh6T29v5Gd2D97PIXWtrcG3MB3ckFktx8Sg+dvArVEgyGVQqIsiBROiYcUU4x6JVzdhzABoIlTtAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4c571920881f6945224ef51375345c38d4ae2fa5299799c7aa370d14adbb60de","last_reissued_at":"2026-07-05T01:59:57.033255Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:59:57.033255Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Case For Adaptive Deep Neural Networks in Edge Computing","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.DC","authors_text":"Blesson Varghese, Francis McNamee, Ivor Spence, Peter Kilpatrick, Schahram Dustadar, Weisong Shi","submitted_at":"2020-08-04T20:23:50Z","abstract_excerpt":"Edge computing offers an additional layer of compute infrastructure closer to the data source before raw data from privacy-sensitive and performance-critical applications is transferred to a cloud data center. Deep Neural Networks (DNNs) are one class of applications that are reported to benefit from collaboratively computing between the edge and the cloud. A DNN is partitioned such that specific layers of the DNN are deployed onto the edge and the cloud to meet performance and privacy objectives. However, there is limited understanding of: (a) whether and how evolving operational conditions ("},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2008.01814","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/2008.01814/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2008.01814","created_at":"2026-07-05T01:59:57.033340+00:00"},{"alias_kind":"arxiv_version","alias_value":"2008.01814v2","created_at":"2026-07-05T01:59:57.033340+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2008.01814","created_at":"2026-07-05T01:59:57.033340+00:00"},{"alias_kind":"pith_short_12","alias_value":"JRLRSIEID5UU","created_at":"2026-07-05T01:59:57.033340+00:00"},{"alias_kind":"pith_short_16","alias_value":"JRLRSIEID5UUKISO","created_at":"2026-07-05T01:59:57.033340+00:00"},{"alias_kind":"pith_short_8","alias_value":"JRLRSIEI","created_at":"2026-07-05T01:59:57.033340+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/JRLRSIEID5UUKISO6UJXKNC4HD","json":"https://pith.science/pith/JRLRSIEID5UUKISO6UJXKNC4HD.json","graph_json":"https://pith.science/api/pith-number/JRLRSIEID5UUKISO6UJXKNC4HD/graph.json","events_json":"https://pith.science/api/pith-number/JRLRSIEID5UUKISO6UJXKNC4HD/events.json","paper":"https://pith.science/paper/JRLRSIEI"},"agent_actions":{"view_html":"https://pith.science/pith/JRLRSIEID5UUKISO6UJXKNC4HD","download_json":"https://pith.science/pith/JRLRSIEID5UUKISO6UJXKNC4HD.json","view_paper":"https://pith.science/paper/JRLRSIEI","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2008.01814&json=true","fetch_graph":"https://pith.science/api/pith-number/JRLRSIEID5UUKISO6UJXKNC4HD/graph.json","fetch_events":"https://pith.science/api/pith-number/JRLRSIEID5UUKISO6UJXKNC4HD/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/JRLRSIEID5UUKISO6UJXKNC4HD/action/timestamp_anchor","attest_storage":"https://pith.science/pith/JRLRSIEID5UUKISO6UJXKNC4HD/action/storage_attestation","attest_author":"https://pith.science/pith/JRLRSIEID5UUKISO6UJXKNC4HD/action/author_attestation","sign_citation":"https://pith.science/pith/JRLRSIEID5UUKISO6UJXKNC4HD/action/citation_signature","submit_replication":"https://pith.science/pith/JRLRSIEID5UUKISO6UJXKNC4HD/action/replication_record"}},"created_at":"2026-07-05T01:59:57.033340+00:00","updated_at":"2026-07-05T01:59:57.033340+00:00"}