{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:IQ3MMPEGK2RWVCD5RWO6X5TBEN","short_pith_number":"pith:IQ3MMPEG","schema_version":"1.0","canonical_sha256":"4436c63c8656a36a887d8d9debf66123574cacd9333f13d6b2e0786b4078643d","source":{"kind":"arxiv","id":"2112.15131","version":1},"attestation_state":"computed","paper":{"title":"Resource-Efficient Deep Learning: A Survey on Model-, Arithmetic-, and Implementation-Level Techniques","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Amir Sabbagh Molahosseini, Cheol-Ho Hong, Hans Vandierendonck, Jesus Martinez del Rincon, Junkyu Lee, Kiril Dichev, Lev Mukhanov, Umar Minhas, Yang Hua","submitted_at":"2021-12-30T17:00:06Z","abstract_excerpt":"Deep learning is pervasive in our daily life, including self-driving cars, virtual assistants, social network services, healthcare services, face recognition, etc. However, deep neural networks demand substantial compute resources during training and inference. The machine learning community has mainly focused on model-level optimizations such as architectural compression of deep learning models, while the system community has focused on implementation-level optimization. In between, various arithmetic-level optimization techniques have been proposed in the arithmetic community. This article p"},"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":"2112.15131","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2021-12-30T17:00:06Z","cross_cats_sorted":[],"title_canon_sha256":"48fecdfc5b8f5c227c2e24252b5ac424f57658c60033008797a6f9f8f1a77475","abstract_canon_sha256":"e3556fb984deec3db65d5bea617393a09dc7e720072ff72c77a3447eab2c0d32"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:13:06.458328Z","signature_b64":"PLTnXjLo6xybIRAezwUH8bNeh/aZonworaUPH3u7lcQXdhKt0/z5p75DmKikHrDaYuR1RTy+As28yJion5IyCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4436c63c8656a36a887d8d9debf66123574cacd9333f13d6b2e0786b4078643d","last_reissued_at":"2026-07-05T08:13:06.457892Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:13:06.457892Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Resource-Efficient Deep Learning: A Survey on Model-, Arithmetic-, and Implementation-Level Techniques","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Amir Sabbagh Molahosseini, Cheol-Ho Hong, Hans Vandierendonck, Jesus Martinez del Rincon, Junkyu Lee, Kiril Dichev, Lev Mukhanov, Umar Minhas, Yang Hua","submitted_at":"2021-12-30T17:00:06Z","abstract_excerpt":"Deep learning is pervasive in our daily life, including self-driving cars, virtual assistants, social network services, healthcare services, face recognition, etc. However, deep neural networks demand substantial compute resources during training and inference. The machine learning community has mainly focused on model-level optimizations such as architectural compression of deep learning models, while the system community has focused on implementation-level optimization. In between, various arithmetic-level optimization techniques have been proposed in the arithmetic community. This article p"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2112.15131","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/2112.15131/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":"2112.15131","created_at":"2026-07-05T08:13:06.457944+00:00"},{"alias_kind":"arxiv_version","alias_value":"2112.15131v1","created_at":"2026-07-05T08:13:06.457944+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2112.15131","created_at":"2026-07-05T08:13:06.457944+00:00"},{"alias_kind":"pith_short_12","alias_value":"IQ3MMPEGK2RW","created_at":"2026-07-05T08:13:06.457944+00:00"},{"alias_kind":"pith_short_16","alias_value":"IQ3MMPEGK2RWVCD5","created_at":"2026-07-05T08:13:06.457944+00:00"},{"alias_kind":"pith_short_8","alias_value":"IQ3MMPEG","created_at":"2026-07-05T08:13:06.457944+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/IQ3MMPEGK2RWVCD5RWO6X5TBEN","json":"https://pith.science/pith/IQ3MMPEGK2RWVCD5RWO6X5TBEN.json","graph_json":"https://pith.science/api/pith-number/IQ3MMPEGK2RWVCD5RWO6X5TBEN/graph.json","events_json":"https://pith.science/api/pith-number/IQ3MMPEGK2RWVCD5RWO6X5TBEN/events.json","paper":"https://pith.science/paper/IQ3MMPEG"},"agent_actions":{"view_html":"https://pith.science/pith/IQ3MMPEGK2RWVCD5RWO6X5TBEN","download_json":"https://pith.science/pith/IQ3MMPEGK2RWVCD5RWO6X5TBEN.json","view_paper":"https://pith.science/paper/IQ3MMPEG","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2112.15131&json=true","fetch_graph":"https://pith.science/api/pith-number/IQ3MMPEGK2RWVCD5RWO6X5TBEN/graph.json","fetch_events":"https://pith.science/api/pith-number/IQ3MMPEGK2RWVCD5RWO6X5TBEN/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/IQ3MMPEGK2RWVCD5RWO6X5TBEN/action/timestamp_anchor","attest_storage":"https://pith.science/pith/IQ3MMPEGK2RWVCD5RWO6X5TBEN/action/storage_attestation","attest_author":"https://pith.science/pith/IQ3MMPEGK2RWVCD5RWO6X5TBEN/action/author_attestation","sign_citation":"https://pith.science/pith/IQ3MMPEGK2RWVCD5RWO6X5TBEN/action/citation_signature","submit_replication":"https://pith.science/pith/IQ3MMPEGK2RWVCD5RWO6X5TBEN/action/replication_record"}},"created_at":"2026-07-05T08:13:06.457944+00:00","updated_at":"2026-07-05T08:13:06.457944+00:00"}