{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:USXFPSZO54GKGQFYWSOFBYGJBZ","short_pith_number":"pith:USXFPSZO","schema_version":"1.0","canonical_sha256":"a4ae57cb2eef0ca340b8b49c50e0c90e63d502f60702e924f018018b33630e82","source":{"kind":"arxiv","id":"2111.00364","version":2},"attestation_state":"computed","paper":{"title":"Sustainable AI: Environmental Implications, Challenges and Opportunities","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.AI","cs.AR"],"primary_cat":"cs.LG","authors_text":"Anastasia Melnikov, Anurag Gupta, Benjamin Lee, Bilge Acun, Bugra Akyildiz, Carole-Jean Wu, Charles Bai, David Brooks, Fiona Aga Behram, Geeta Chauhan, Gloria Chang, Hsien-Hsin S. Lee, James Huang, Joe Spisak, Kim Hazelwood, Kiwan Maeng, Maximilian Balandat, Michael Gschwind, Mike Rabbat, Myle Ott, Newsha Ardalani, Ramya Raghavendra, Ravi Jain, Salvatore Candido, Udit Gupta","submitted_at":"2021-10-30T23:36:10Z","abstract_excerpt":"This paper explores the environmental impact of the super-linear growth trends for AI from a holistic perspective, spanning Data, Algorithms, and System Hardware. We characterize the carbon footprint of AI computing by examining the model development cycle across industry-scale machine learning use cases and, at the same time, considering the life cycle of system hardware. Taking a step further, we capture the operational and manufacturing carbon footprint of AI computing and present an end-to-end analysis for what and how hardware-software design and at-scale optimization can help reduce the "},"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":"2111.00364","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.LG","submitted_at":"2021-10-30T23:36:10Z","cross_cats_sorted":["cs.AI","cs.AR"],"title_canon_sha256":"bbf1486c149eee1b0bf450088cb3b3165c43823ebea54bdd37710f1603aba6ad","abstract_canon_sha256":"f152fba79fe92b9a513c77e88c5270104ab11f672b3617617a874715d5b08a4a"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:47:02.107574Z","signature_b64":"/8JPfYQJBjqyswgSRp3Hc3FENvvScTB0chcCJ9LunqNIefGYYIrVmPHU445ZMGC7MWW42QOsVef9GgXiG9zNDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a4ae57cb2eef0ca340b8b49c50e0c90e63d502f60702e924f018018b33630e82","last_reissued_at":"2026-07-05T03:47:02.107010Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:47:02.107010Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Sustainable AI: Environmental Implications, Challenges and Opportunities","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.AI","cs.AR"],"primary_cat":"cs.LG","authors_text":"Anastasia Melnikov, Anurag Gupta, Benjamin Lee, Bilge Acun, Bugra Akyildiz, Carole-Jean Wu, Charles Bai, David Brooks, Fiona Aga Behram, Geeta Chauhan, Gloria Chang, Hsien-Hsin S. Lee, James Huang, Joe Spisak, Kim Hazelwood, Kiwan Maeng, Maximilian Balandat, Michael Gschwind, Mike Rabbat, Myle Ott, Newsha Ardalani, Ramya Raghavendra, Ravi Jain, Salvatore Candido, Udit Gupta","submitted_at":"2021-10-30T23:36:10Z","abstract_excerpt":"This paper explores the environmental impact of the super-linear growth trends for AI from a holistic perspective, spanning Data, Algorithms, and System Hardware. We characterize the carbon footprint of AI computing by examining the model development cycle across industry-scale machine learning use cases and, at the same time, considering the life cycle of system hardware. Taking a step further, we capture the operational and manufacturing carbon footprint of AI computing and present an end-to-end analysis for what and how hardware-software design and at-scale optimization can help reduce the "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2111.00364","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/2111.00364/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":"2111.00364","created_at":"2026-07-05T03:47:02.107067+00:00"},{"alias_kind":"arxiv_version","alias_value":"2111.00364v2","created_at":"2026-07-05T03:47:02.107067+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2111.00364","created_at":"2026-07-05T03:47:02.107067+00:00"},{"alias_kind":"pith_short_12","alias_value":"USXFPSZO54GK","created_at":"2026-07-05T03:47:02.107067+00:00"},{"alias_kind":"pith_short_16","alias_value":"USXFPSZO54GKGQFY","created_at":"2026-07-05T03:47:02.107067+00:00"},{"alias_kind":"pith_short_8","alias_value":"USXFPSZO","created_at":"2026-07-05T03:47:02.107067+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":6,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.27652","citing_title":"Carbon-Aware Mapping and Scheduling for Deadline-Constrained Workflows","ref_index":31,"is_internal_anchor":false},{"citing_arxiv_id":"2606.27841","citing_title":"WattLayer: Get Layers Right to Estimate Inference Energy of Neural Networks","ref_index":4,"is_internal_anchor":false},{"citing_arxiv_id":"2503.13469","citing_title":"Conditional Electrocardiogram Generation Using Hierarchical Variational Autoencoders","ref_index":37,"is_internal_anchor":false},{"citing_arxiv_id":"2205.00445","citing_title":"MRKL Systems: A modular, neuro-symbolic architecture that combines large language models, external knowledge sources and discrete reasoning","ref_index":20,"is_internal_anchor":false},{"citing_arxiv_id":"2605.11733","citing_title":"Position: LLM Inference Should Be Evaluated as Energy-to-Token Production","ref_index":12,"is_internal_anchor":false},{"citing_arxiv_id":"2604.04745","citing_title":"The Energy Cost of Execution-Idle in GPU Clusters","ref_index":58,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/USXFPSZO54GKGQFYWSOFBYGJBZ","json":"https://pith.science/pith/USXFPSZO54GKGQFYWSOFBYGJBZ.json","graph_json":"https://pith.science/api/pith-number/USXFPSZO54GKGQFYWSOFBYGJBZ/graph.json","events_json":"https://pith.science/api/pith-number/USXFPSZO54GKGQFYWSOFBYGJBZ/events.json","paper":"https://pith.science/paper/USXFPSZO"},"agent_actions":{"view_html":"https://pith.science/pith/USXFPSZO54GKGQFYWSOFBYGJBZ","download_json":"https://pith.science/pith/USXFPSZO54GKGQFYWSOFBYGJBZ.json","view_paper":"https://pith.science/paper/USXFPSZO","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2111.00364&json=true","fetch_graph":"https://pith.science/api/pith-number/USXFPSZO54GKGQFYWSOFBYGJBZ/graph.json","fetch_events":"https://pith.science/api/pith-number/USXFPSZO54GKGQFYWSOFBYGJBZ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/USXFPSZO54GKGQFYWSOFBYGJBZ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/USXFPSZO54GKGQFYWSOFBYGJBZ/action/storage_attestation","attest_author":"https://pith.science/pith/USXFPSZO54GKGQFYWSOFBYGJBZ/action/author_attestation","sign_citation":"https://pith.science/pith/USXFPSZO54GKGQFYWSOFBYGJBZ/action/citation_signature","submit_replication":"https://pith.science/pith/USXFPSZO54GKGQFYWSOFBYGJBZ/action/replication_record"}},"created_at":"2026-07-05T03:47:02.107067+00:00","updated_at":"2026-07-05T03:47:02.107067+00:00"}