{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:FZNCZ4BCMYMXQOF3P7TW2QSIUV","short_pith_number":"pith:FZNCZ4BC","canonical_record":{"source":{"id":"2405.06038","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2024-05-09T18:17:25Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"79015b43005d55695863b0978d2fcc8e1a969c0ed4c97142e4a86211e8751a8b","abstract_canon_sha256":"64a2a8bbcb8792cb79edf0407457b370a8f223ed4d5af4ceb89b83d14ca9b848"},"schema_version":"1.0"},"canonical_sha256":"2e5a2cf02266197838bb7fe76d4248a5780ca31f829e482f13d61e419c2cc1a3","source":{"kind":"arxiv","id":"2405.06038","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2405.06038","created_at":"2026-07-05T08:17:34Z"},{"alias_kind":"arxiv_version","alias_value":"2405.06038v1","created_at":"2026-07-05T08:17:34Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2405.06038","created_at":"2026-07-05T08:17:34Z"},{"alias_kind":"pith_short_12","alias_value":"FZNCZ4BCMYMX","created_at":"2026-07-05T08:17:34Z"},{"alias_kind":"pith_short_16","alias_value":"FZNCZ4BCMYMXQOF3","created_at":"2026-07-05T08:17:34Z"},{"alias_kind":"pith_short_8","alias_value":"FZNCZ4BC","created_at":"2026-07-05T08:17:34Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:FZNCZ4BCMYMXQOF3P7TW2QSIUV","target":"record","payload":{"canonical_record":{"source":{"id":"2405.06038","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2024-05-09T18:17:25Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"79015b43005d55695863b0978d2fcc8e1a969c0ed4c97142e4a86211e8751a8b","abstract_canon_sha256":"64a2a8bbcb8792cb79edf0407457b370a8f223ed4d5af4ceb89b83d14ca9b848"},"schema_version":"1.0"},"canonical_sha256":"2e5a2cf02266197838bb7fe76d4248a5780ca31f829e482f13d61e419c2cc1a3","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:17:34.575894Z","signature_b64":"NrX+Q2jGiG8s8uzLg5TJGT8tjMeDBzfV8ewxpko+vp130xXi70HUPjyKSkNEcyUod0t8eECt8FN0V2IZ5S2RBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2e5a2cf02266197838bb7fe76d4248a5780ca31f829e482f13d61e419c2cc1a3","last_reissued_at":"2026-07-05T08:17:34.575422Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:17:34.575422Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2405.06038","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T08:17:34Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"wSaEokm8OSqWXp3zNV3tjtIls8EWQf4Iqg6S8zNyDb6Vf9NZyy7r4KN9rzRkiuucQiuR+FhazIYFzqpFvi3+AQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T20:43:39.611930Z"},"content_sha256":"7ba59e04e8cd8cceca85d7e5e95a4516136a1810f81903241110530fa64c2afd","schema_version":"1.0","event_id":"sha256:7ba59e04e8cd8cceca85d7e5e95a4516136a1810f81903241110530fa64c2afd"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:FZNCZ4BCMYMXQOF3P7TW2QSIUV","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"From Algorithm to Hardware: A Survey on Efficient and Safe Deployment of Deep Neural Networks","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Chao Jin, Chunyun Chen, Jie Lin, Kaixin Xu, Manas Gupta, Min Wu, Mohamed M. Sabry Aly, Qing Xu, Xiaoli Li, Xue Geng, Xulei Yang, Zhenghua Chen, Zhe Wang","submitted_at":"2024-05-09T18:17:25Z","abstract_excerpt":"Deep neural networks (DNNs) have been widely used in many artificial intelligence (AI) tasks. However, deploying them brings significant challenges due to the huge cost of memory, energy, and computation. To address these challenges, researchers have developed various model compression techniques such as model quantization and model pruning. Recently, there has been a surge in research of compression methods to achieve model efficiency while retaining the performance. Furthermore, more and more works focus on customizing the DNN hardware accelerators to better leverage the model compression te"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2405.06038","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/2405.06038/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T08:17:34Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"MTVpNh73xLOR3m+F5blmmF1z7sIAR5pcBL9B8ICuNvPOPINHgc+JyzWtgJOF7KDVrmKQcjdOmX/Y4E2+JOh9Dw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T20:43:39.612934Z"},"content_sha256":"3cf67db011cbef79fb8f7e83461774a005c7f9872ad7094277424b8793727ddc","schema_version":"1.0","event_id":"sha256:3cf67db011cbef79fb8f7e83461774a005c7f9872ad7094277424b8793727ddc"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/FZNCZ4BCMYMXQOF3P7TW2QSIUV/bundle.json","state_url":"https://pith.science/pith/FZNCZ4BCMYMXQOF3P7TW2QSIUV/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/FZNCZ4BCMYMXQOF3P7TW2QSIUV/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-08T20:43:39Z","links":{"resolver":"https://pith.science/pith/FZNCZ4BCMYMXQOF3P7TW2QSIUV","bundle":"https://pith.science/pith/FZNCZ4BCMYMXQOF3P7TW2QSIUV/bundle.json","state":"https://pith.science/pith/FZNCZ4BCMYMXQOF3P7TW2QSIUV/state.json","well_known_bundle":"https://pith.science/.well-known/pith/FZNCZ4BCMYMXQOF3P7TW2QSIUV/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:FZNCZ4BCMYMXQOF3P7TW2QSIUV","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":"64a2a8bbcb8792cb79edf0407457b370a8f223ed4d5af4ceb89b83d14ca9b848","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2024-05-09T18:17:25Z","title_canon_sha256":"79015b43005d55695863b0978d2fcc8e1a969c0ed4c97142e4a86211e8751a8b"},"schema_version":"1.0","source":{"id":"2405.06038","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2405.06038","created_at":"2026-07-05T08:17:34Z"},{"alias_kind":"arxiv_version","alias_value":"2405.06038v1","created_at":"2026-07-05T08:17:34Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2405.06038","created_at":"2026-07-05T08:17:34Z"},{"alias_kind":"pith_short_12","alias_value":"FZNCZ4BCMYMX","created_at":"2026-07-05T08:17:34Z"},{"alias_kind":"pith_short_16","alias_value":"FZNCZ4BCMYMXQOF3","created_at":"2026-07-05T08:17:34Z"},{"alias_kind":"pith_short_8","alias_value":"FZNCZ4BC","created_at":"2026-07-05T08:17:34Z"}],"graph_snapshots":[{"event_id":"sha256:3cf67db011cbef79fb8f7e83461774a005c7f9872ad7094277424b8793727ddc","target":"graph","created_at":"2026-07-05T08:17:34Z","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/2405.06038/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Deep neural networks (DNNs) have been widely used in many artificial intelligence (AI) tasks. However, deploying them brings significant challenges due to the huge cost of memory, energy, and computation. To address these challenges, researchers have developed various model compression techniques such as model quantization and model pruning. Recently, there has been a surge in research of compression methods to achieve model efficiency while retaining the performance. Furthermore, more and more works focus on customizing the DNN hardware accelerators to better leverage the model compression te","authors_text":"Chao Jin, Chunyun Chen, Jie Lin, Kaixin Xu, Manas Gupta, Min Wu, Mohamed M. Sabry Aly, Qing Xu, Xiaoli Li, Xue Geng, Xulei Yang, Zhenghua Chen, Zhe Wang","cross_cats":["cs.AI"],"headline":"","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2024-05-09T18:17:25Z","title":"From Algorithm to Hardware: A Survey on Efficient and Safe Deployment of Deep Neural Networks"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2405.06038","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":"sha256:7ba59e04e8cd8cceca85d7e5e95a4516136a1810f81903241110530fa64c2afd","target":"record","created_at":"2026-07-05T08:17:34Z","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":"64a2a8bbcb8792cb79edf0407457b370a8f223ed4d5af4ceb89b83d14ca9b848","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2024-05-09T18:17:25Z","title_canon_sha256":"79015b43005d55695863b0978d2fcc8e1a969c0ed4c97142e4a86211e8751a8b"},"schema_version":"1.0","source":{"id":"2405.06038","kind":"arxiv","version":1}},"canonical_sha256":"2e5a2cf02266197838bb7fe76d4248a5780ca31f829e482f13d61e419c2cc1a3","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"2e5a2cf02266197838bb7fe76d4248a5780ca31f829e482f13d61e419c2cc1a3","first_computed_at":"2026-07-05T08:17:34.575422Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T08:17:34.575422Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"NrX+Q2jGiG8s8uzLg5TJGT8tjMeDBzfV8ewxpko+vp130xXi70HUPjyKSkNEcyUod0t8eECt8FN0V2IZ5S2RBQ==","signature_status":"signed_v1","signed_at":"2026-07-05T08:17:34.575894Z","signed_message":"canonical_sha256_bytes"},"source_id":"2405.06038","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:7ba59e04e8cd8cceca85d7e5e95a4516136a1810f81903241110530fa64c2afd","sha256:3cf67db011cbef79fb8f7e83461774a005c7f9872ad7094277424b8793727ddc"],"state_sha256":"64f0271213eec7b4e5ea2dce4fe3779ffaf5443a82726ad43a1f466c742db0e0"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"CpdEJzrGFPrvTTxzsjev8Nsr5qTes8EI1D+JoOH7ByAU91hjBYu/XCRa8dERbxBiVZ+u/wlHHDfjGyGgXa81AQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-08T20:43:39.650904Z","bundle_sha256":"f94fa702be9ed91418224b4c0feaecbe0c0519e033380b506c2084bdfc187e43"}}