{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:GCWXH7JYPBWZZFQNQ6BJTXDBBJ","short_pith_number":"pith:GCWXH7JY","schema_version":"1.0","canonical_sha256":"30ad73fd38786d9c960d878299dc610a7d08fda9fefd7879beca5beb49abb344","source":{"kind":"arxiv","id":"2404.19209","version":1},"attestation_state":"computed","paper":{"title":"AdaOper: Energy-efficient and Responsive Concurrent DNN Inference on Mobile Devices","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.DC","authors_text":"Bin Guo, Sicong Liu, Wentao Zhou, Yasan Ding, Yu Zhang, Zheng Lin, Zhiwen Yu","submitted_at":"2024-04-30T02:22:31Z","abstract_excerpt":"Deep neural network (DNN) has driven extensive applications in mobile technology. However, for long-running mobile apps like voice assistants or video applications on smartphones, energy efficiency is critical for battery-powered devices. The rise of heterogeneous processors in mobile devices today has introduced new challenges for optimizing energy efficiency. Our key insight is that partitioning computations across different processors for parallelism and speedup doesn't necessarily correlate with energy consumption optimization and may even increase it. To address this, we present AdaOper, "},"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":"2404.19209","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.DC","submitted_at":"2024-04-30T02:22:31Z","cross_cats_sorted":[],"title_canon_sha256":"74ad438fcd8f6eaece2d93650303ef5e1e681bf1a601cfa2fb1fe289513582b7","abstract_canon_sha256":"080ee928723c7c8ae2f1f115bb966890b918f33c8069e6f32d18deaa20ed612f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:13:39.958062Z","signature_b64":"SnDVZRy6/XEqukgsujknFvTo02JWy6f9zgDv6IxP2oZYkshk/WWjSyh6+A7Nkaeh3pNB8opJ8W9bNfoRE2nYAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"30ad73fd38786d9c960d878299dc610a7d08fda9fefd7879beca5beb49abb344","last_reissued_at":"2026-07-05T08:13:39.957553Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:13:39.957553Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"AdaOper: Energy-efficient and Responsive Concurrent DNN Inference on Mobile Devices","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.DC","authors_text":"Bin Guo, Sicong Liu, Wentao Zhou, Yasan Ding, Yu Zhang, Zheng Lin, Zhiwen Yu","submitted_at":"2024-04-30T02:22:31Z","abstract_excerpt":"Deep neural network (DNN) has driven extensive applications in mobile technology. However, for long-running mobile apps like voice assistants or video applications on smartphones, energy efficiency is critical for battery-powered devices. The rise of heterogeneous processors in mobile devices today has introduced new challenges for optimizing energy efficiency. Our key insight is that partitioning computations across different processors for parallelism and speedup doesn't necessarily correlate with energy consumption optimization and may even increase it. To address this, we present AdaOper, "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2404.19209","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/2404.19209/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":"2404.19209","created_at":"2026-07-05T08:13:39.957620+00:00"},{"alias_kind":"arxiv_version","alias_value":"2404.19209v1","created_at":"2026-07-05T08:13:39.957620+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2404.19209","created_at":"2026-07-05T08:13:39.957620+00:00"},{"alias_kind":"pith_short_12","alias_value":"GCWXH7JYPBWZ","created_at":"2026-07-05T08:13:39.957620+00:00"},{"alias_kind":"pith_short_16","alias_value":"GCWXH7JYPBWZZFQN","created_at":"2026-07-05T08:13:39.957620+00:00"},{"alias_kind":"pith_short_8","alias_value":"GCWXH7JY","created_at":"2026-07-05T08:13:39.957620+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/GCWXH7JYPBWZZFQNQ6BJTXDBBJ","json":"https://pith.science/pith/GCWXH7JYPBWZZFQNQ6BJTXDBBJ.json","graph_json":"https://pith.science/api/pith-number/GCWXH7JYPBWZZFQNQ6BJTXDBBJ/graph.json","events_json":"https://pith.science/api/pith-number/GCWXH7JYPBWZZFQNQ6BJTXDBBJ/events.json","paper":"https://pith.science/paper/GCWXH7JY"},"agent_actions":{"view_html":"https://pith.science/pith/GCWXH7JYPBWZZFQNQ6BJTXDBBJ","download_json":"https://pith.science/pith/GCWXH7JYPBWZZFQNQ6BJTXDBBJ.json","view_paper":"https://pith.science/paper/GCWXH7JY","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2404.19209&json=true","fetch_graph":"https://pith.science/api/pith-number/GCWXH7JYPBWZZFQNQ6BJTXDBBJ/graph.json","fetch_events":"https://pith.science/api/pith-number/GCWXH7JYPBWZZFQNQ6BJTXDBBJ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/GCWXH7JYPBWZZFQNQ6BJTXDBBJ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/GCWXH7JYPBWZZFQNQ6BJTXDBBJ/action/storage_attestation","attest_author":"https://pith.science/pith/GCWXH7JYPBWZZFQNQ6BJTXDBBJ/action/author_attestation","sign_citation":"https://pith.science/pith/GCWXH7JYPBWZZFQNQ6BJTXDBBJ/action/citation_signature","submit_replication":"https://pith.science/pith/GCWXH7JYPBWZZFQNQ6BJTXDBBJ/action/replication_record"}},"created_at":"2026-07-05T08:13:39.957620+00:00","updated_at":"2026-07-05T08:13:39.957620+00:00"}