{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2021:WK2QW4CKUDUQNH756ARQVQ5MSF","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"0c2b85f82ae81318b2a8a3f91989ac082a8f460f7e9940d58210bd870e360408","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2021-08-25T03:50:46Z","title_canon_sha256":"56cc7f06e2b40a77e312d6bc0a65ee39ca7c0ed6058e5fc282bfb9b01138bdfa"},"schema_version":"1.0","source":{"id":"2108.11033","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2108.11033","created_at":"2026-07-05T03:08:48Z"},{"alias_kind":"arxiv_version","alias_value":"2108.11033v1","created_at":"2026-07-05T03:08:48Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2108.11033","created_at":"2026-07-05T03:08:48Z"},{"alias_kind":"pith_short_12","alias_value":"WK2QW4CKUDUQ","created_at":"2026-07-05T03:08:48Z"},{"alias_kind":"pith_short_16","alias_value":"WK2QW4CKUDUQNH75","created_at":"2026-07-05T03:08:48Z"},{"alias_kind":"pith_short_8","alias_value":"WK2QW4CK","created_at":"2026-07-05T03:08:48Z"}],"graph_snapshots":[{"event_id":"sha256:8bb3c6baff4e90b8bd14e6a0acc56eade5aaaae5e0b1d44cd9599ecc129c7df7","target":"graph","created_at":"2026-07-05T03:08:48Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2108.11033/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"It is appealing but challenging to achieve real-time deep neural network (DNN) inference on mobile devices because even the powerful modern mobile devices are considered as ``resource-constrained'' when executing large-scale DNNs. It necessitates the sparse model inference via weight pruning, i.e., DNN weight sparsity, and it is desirable to design a new DNN weight sparsity scheme that can facilitate real-time inference on mobile devices while preserving a high sparse model accuracy. This paper designs a novel mobile inference acceleration framework GRIM that is General to both convolutional n","authors_text":"Bin Ren, Gang Zhou, Peiyan Dong, Wei Niu, Xiaolong Ma, Xuehai Qian, Xue Lin, Yanzhi Wang, Zhengang Li","cross_cats":["cs.AI"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2021-08-25T03:50:46Z","title":"GRIM: A General, Real-Time Deep Learning Inference Framework for Mobile Devices based on Fine-Grained Structured Weight Sparsity"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2108.11033","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:c7f3c41761c0b2a3ed4efc2d02105859682860a1f4e94f137c90134033767164","target":"record","created_at":"2026-07-05T03:08:48Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"0c2b85f82ae81318b2a8a3f91989ac082a8f460f7e9940d58210bd870e360408","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2021-08-25T03:50:46Z","title_canon_sha256":"56cc7f06e2b40a77e312d6bc0a65ee39ca7c0ed6058e5fc282bfb9b01138bdfa"},"schema_version":"1.0","source":{"id":"2108.11033","kind":"arxiv","version":1}},"canonical_sha256":"b2b50b704aa0e9069ffdf0230ac3ac91577e4be9515e65af67ddde5b8f3df061","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"b2b50b704aa0e9069ffdf0230ac3ac91577e4be9515e65af67ddde5b8f3df061","first_computed_at":"2026-07-05T03:08:48.603266Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T03:08:48.603266Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"OhqeekTVl/+eo1hyF3KxnwISzmFsGvVXahssbfFt//Lr27kdjKZC7+1Xm8r5nvfqca4skgHxKpAvE9I/W3HfDQ==","signature_status":"signed_v1","signed_at":"2026-07-05T03:08:48.603643Z","signed_message":"canonical_sha256_bytes"},"source_id":"2108.11033","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:c7f3c41761c0b2a3ed4efc2d02105859682860a1f4e94f137c90134033767164","sha256:8bb3c6baff4e90b8bd14e6a0acc56eade5aaaae5e0b1d44cd9599ecc129c7df7"],"state_sha256":"05e3e69f1f2ee7165cbb6e03033eff16e0692676b16155ba5f625ac10da378cf"}