{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:CFGQGJ3FDKCATBQVYOCR2EUIDA","short_pith_number":"pith:CFGQGJ3F","schema_version":"1.0","canonical_sha256":"114d0327651a84098615c3851d12881829f2f0fd956629b8abfd8aafbac6b201","source":{"kind":"arxiv","id":"2406.01414","version":2},"attestation_state":"computed","paper":{"title":"CE-NAS: An End-to-End Carbon-Efficient Neural Architecture Search Framework","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["eess.SP"],"primary_cat":"cs.LG","authors_text":"Bo Jiang, Tian Guo, Yiyang Zhao, Yunzhuo Liu","submitted_at":"2024-06-03T15:13:21Z","abstract_excerpt":"This work presents a novel approach to neural architecture search (NAS) that aims to increase carbon efficiency for the model design process. The proposed framework CE-NAS addresses the key challenge of high carbon cost associated with NAS by exploring the carbon emission variations of energy and energy differences of different NAS algorithms. At the high level, CE-NAS leverages a reinforcement-learning agent to dynamically adjust GPU resources based on carbon intensity, predicted by a time-series transformer, to balance energy-efficient sampling and energy-intensive evaluation tasks. Furtherm"},"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":"2406.01414","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-06-03T15:13:21Z","cross_cats_sorted":["eess.SP"],"title_canon_sha256":"9a8bb36ae19db0ff64fa877ef959c8cc093a3b91210a7d75817173066708b84c","abstract_canon_sha256":"f75ce224fd84e85a55315135038e33fdb37d34412829d2acd0041cad0918103b"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:45:29.200328Z","signature_b64":"RBuH5t20HIwO+8uzi3UYHqU3SifGimzgO1+enj7rS91ARWVOadeKhMd3pZDaUYH1bWIf3HyB5+UpLCFpQ4qDCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"114d0327651a84098615c3851d12881829f2f0fd956629b8abfd8aafbac6b201","last_reissued_at":"2026-07-05T08:45:29.199973Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:45:29.199973Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"CE-NAS: An End-to-End Carbon-Efficient Neural Architecture Search Framework","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["eess.SP"],"primary_cat":"cs.LG","authors_text":"Bo Jiang, Tian Guo, Yiyang Zhao, Yunzhuo Liu","submitted_at":"2024-06-03T15:13:21Z","abstract_excerpt":"This work presents a novel approach to neural architecture search (NAS) that aims to increase carbon efficiency for the model design process. The proposed framework CE-NAS addresses the key challenge of high carbon cost associated with NAS by exploring the carbon emission variations of energy and energy differences of different NAS algorithms. At the high level, CE-NAS leverages a reinforcement-learning agent to dynamically adjust GPU resources based on carbon intensity, predicted by a time-series transformer, to balance energy-efficient sampling and energy-intensive evaluation tasks. Furtherm"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2406.01414","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/2406.01414/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":"2406.01414","created_at":"2026-07-05T08:45:29.200028+00:00"},{"alias_kind":"arxiv_version","alias_value":"2406.01414v2","created_at":"2026-07-05T08:45:29.200028+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2406.01414","created_at":"2026-07-05T08:45:29.200028+00:00"},{"alias_kind":"pith_short_12","alias_value":"CFGQGJ3FDKCA","created_at":"2026-07-05T08:45:29.200028+00:00"},{"alias_kind":"pith_short_16","alias_value":"CFGQGJ3FDKCATBQV","created_at":"2026-07-05T08:45:29.200028+00:00"},{"alias_kind":"pith_short_8","alias_value":"CFGQGJ3F","created_at":"2026-07-05T08:45:29.200028+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2506.01774","citing_title":"Greening AI-enabled Systems with Software Engineering: A Research Agenda for Environmentally Sustainable AI Practices","ref_index":48,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/CFGQGJ3FDKCATBQVYOCR2EUIDA","json":"https://pith.science/pith/CFGQGJ3FDKCATBQVYOCR2EUIDA.json","graph_json":"https://pith.science/api/pith-number/CFGQGJ3FDKCATBQVYOCR2EUIDA/graph.json","events_json":"https://pith.science/api/pith-number/CFGQGJ3FDKCATBQVYOCR2EUIDA/events.json","paper":"https://pith.science/paper/CFGQGJ3F"},"agent_actions":{"view_html":"https://pith.science/pith/CFGQGJ3FDKCATBQVYOCR2EUIDA","download_json":"https://pith.science/pith/CFGQGJ3FDKCATBQVYOCR2EUIDA.json","view_paper":"https://pith.science/paper/CFGQGJ3F","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2406.01414&json=true","fetch_graph":"https://pith.science/api/pith-number/CFGQGJ3FDKCATBQVYOCR2EUIDA/graph.json","fetch_events":"https://pith.science/api/pith-number/CFGQGJ3FDKCATBQVYOCR2EUIDA/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/CFGQGJ3FDKCATBQVYOCR2EUIDA/action/timestamp_anchor","attest_storage":"https://pith.science/pith/CFGQGJ3FDKCATBQVYOCR2EUIDA/action/storage_attestation","attest_author":"https://pith.science/pith/CFGQGJ3FDKCATBQVYOCR2EUIDA/action/author_attestation","sign_citation":"https://pith.science/pith/CFGQGJ3FDKCATBQVYOCR2EUIDA/action/citation_signature","submit_replication":"https://pith.science/pith/CFGQGJ3FDKCATBQVYOCR2EUIDA/action/replication_record"}},"created_at":"2026-07-05T08:45:29.200028+00:00","updated_at":"2026-07-05T08:45:29.200028+00:00"}