{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:NVZKH2XJPRWXEHZ7KI2XCMMYGC","short_pith_number":"pith:NVZKH2XJ","canonical_record":{"source":{"id":"2409.09708","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-09-15T12:14:24Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"38809bdb73c1c377b790360b6ac59f1ec7cf176d942095a38afdd3a39c606090","abstract_canon_sha256":"1a556d8e42859e853f33fc828387ddd8b7ac5cde8954156f1bc66ce4aceee6dd"},"schema_version":"1.0"},"canonical_sha256":"6d72a3eae97c6d721f3f523571319830ac2b9ff01780522d7edafb085b4525aa","source":{"kind":"arxiv","id":"2409.09708","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2409.09708","created_at":"2026-07-05T09:07:13Z"},{"alias_kind":"arxiv_version","alias_value":"2409.09708v1","created_at":"2026-07-05T09:07:13Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2409.09708","created_at":"2026-07-05T09:07:13Z"},{"alias_kind":"pith_short_12","alias_value":"NVZKH2XJPRWX","created_at":"2026-07-05T09:07:13Z"},{"alias_kind":"pith_short_16","alias_value":"NVZKH2XJPRWXEHZ7","created_at":"2026-07-05T09:07:13Z"},{"alias_kind":"pith_short_8","alias_value":"NVZKH2XJ","created_at":"2026-07-05T09:07:13Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:NVZKH2XJPRWXEHZ7KI2XCMMYGC","target":"record","payload":{"canonical_record":{"source":{"id":"2409.09708","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-09-15T12:14:24Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"38809bdb73c1c377b790360b6ac59f1ec7cf176d942095a38afdd3a39c606090","abstract_canon_sha256":"1a556d8e42859e853f33fc828387ddd8b7ac5cde8954156f1bc66ce4aceee6dd"},"schema_version":"1.0"},"canonical_sha256":"6d72a3eae97c6d721f3f523571319830ac2b9ff01780522d7edafb085b4525aa","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:07:13.368232Z","signature_b64":"zGsy0Tm4ANVxXlWfEqVUBIHpSPEyMwadZGK0KiJ3UTzLzHhJkRCHZSQk/2QFcI/LdLeeywSbjf8w1r3EwheODQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6d72a3eae97c6d721f3f523571319830ac2b9ff01780522d7edafb085b4525aa","last_reissued_at":"2026-07-05T09:07:13.367797Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:07:13.367797Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2409.09708","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":"2026-07-05T09:07:13Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"h6txqm6Jqa15/az4xE1soVUug2z2pc94H5IwnTL6hpnPVKuhDZo3RqJDIfnNR9YQTmJ+4EicNJRKD+Xjah8WAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-06T20:24:50.090406Z"},"content_sha256":"969e5952c7c2386db5dbdbe9bfc2b8660a387031fc81564959f01e6a2e2a3304","schema_version":"1.0","event_id":"sha256:969e5952c7c2386db5dbdbe9bfc2b8660a387031fc81564959f01e6a2e2a3304"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:NVZKH2XJPRWXEHZ7KI2XCMMYGC","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"ELSA: Exploiting Layer-wise N:M Sparsity for Vision Transformer Acceleration","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Chi-Chih Chang, Diana Marculescu, Endri Taka, Kai-Chiang Wu, Ning-Chi Huang, Wei-Cheng Lin","submitted_at":"2024-09-15T12:14:24Z","abstract_excerpt":"$N{:}M$ sparsity is an emerging model compression method supported by more and more accelerators to speed up sparse matrix multiplication in deep neural networks. Most existing $N{:}M$ sparsity methods compress neural networks with a uniform setting for all layers in a network or heuristically determine the layer-wise configuration by considering the number of parameters in each layer. However, very few methods have been designed for obtaining a layer-wise customized $N{:}M$ sparse configuration for vision transformers (ViTs), which usually consist of transformer blocks involving the same numb"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2409.09708","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/2409.09708/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":"2026-07-05T09:07:13Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"FpoyXVzS9oMML/gf9Nv4SQFyYPbkCWI68Y808+xW3yBKH18wZZDNal0xqwXfKiJaqQxVsHeRRZ9PcMAQTwd6AQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-06T20:24:50.090784Z"},"content_sha256":"8c5719568deb7ba00eabfa01b746a81f8fccdc829193d8ba03e564db80c797c9","schema_version":"1.0","event_id":"sha256:8c5719568deb7ba00eabfa01b746a81f8fccdc829193d8ba03e564db80c797c9"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/NVZKH2XJPRWXEHZ7KI2XCMMYGC/bundle.json","state_url":"https://pith.science/pith/NVZKH2XJPRWXEHZ7KI2XCMMYGC/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/NVZKH2XJPRWXEHZ7KI2XCMMYGC/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":"2026-07-06T20:24:50Z","links":{"resolver":"https://pith.science/pith/NVZKH2XJPRWXEHZ7KI2XCMMYGC","bundle":"https://pith.science/pith/NVZKH2XJPRWXEHZ7KI2XCMMYGC/bundle.json","state":"https://pith.science/pith/NVZKH2XJPRWXEHZ7KI2XCMMYGC/state.json","well_known_bundle":"https://pith.science/.well-known/pith/NVZKH2XJPRWXEHZ7KI2XCMMYGC/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:NVZKH2XJPRWXEHZ7KI2XCMMYGC","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":"1a556d8e42859e853f33fc828387ddd8b7ac5cde8954156f1bc66ce4aceee6dd","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-09-15T12:14:24Z","title_canon_sha256":"38809bdb73c1c377b790360b6ac59f1ec7cf176d942095a38afdd3a39c606090"},"schema_version":"1.0","source":{"id":"2409.09708","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2409.09708","created_at":"2026-07-05T09:07:13Z"},{"alias_kind":"arxiv_version","alias_value":"2409.09708v1","created_at":"2026-07-05T09:07:13Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2409.09708","created_at":"2026-07-05T09:07:13Z"},{"alias_kind":"pith_short_12","alias_value":"NVZKH2XJPRWX","created_at":"2026-07-05T09:07:13Z"},{"alias_kind":"pith_short_16","alias_value":"NVZKH2XJPRWXEHZ7","created_at":"2026-07-05T09:07:13Z"},{"alias_kind":"pith_short_8","alias_value":"NVZKH2XJ","created_at":"2026-07-05T09:07:13Z"}],"graph_snapshots":[{"event_id":"sha256:8c5719568deb7ba00eabfa01b746a81f8fccdc829193d8ba03e564db80c797c9","target":"graph","created_at":"2026-07-05T09:07:13Z","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/2409.09708/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"$N{:}M$ sparsity is an emerging model compression method supported by more and more accelerators to speed up sparse matrix multiplication in deep neural networks. Most existing $N{:}M$ sparsity methods compress neural networks with a uniform setting for all layers in a network or heuristically determine the layer-wise configuration by considering the number of parameters in each layer. However, very few methods have been designed for obtaining a layer-wise customized $N{:}M$ sparse configuration for vision transformers (ViTs), which usually consist of transformer blocks involving the same numb","authors_text":"Chi-Chih Chang, Diana Marculescu, Endri Taka, Kai-Chiang Wu, Ning-Chi Huang, Wei-Cheng Lin","cross_cats":["cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-09-15T12:14:24Z","title":"ELSA: Exploiting Layer-wise N:M Sparsity for Vision Transformer Acceleration"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2409.09708","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":"sha256:969e5952c7c2386db5dbdbe9bfc2b8660a387031fc81564959f01e6a2e2a3304","target":"record","created_at":"2026-07-05T09:07:13Z","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":"1a556d8e42859e853f33fc828387ddd8b7ac5cde8954156f1bc66ce4aceee6dd","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-09-15T12:14:24Z","title_canon_sha256":"38809bdb73c1c377b790360b6ac59f1ec7cf176d942095a38afdd3a39c606090"},"schema_version":"1.0","source":{"id":"2409.09708","kind":"arxiv","version":1}},"canonical_sha256":"6d72a3eae97c6d721f3f523571319830ac2b9ff01780522d7edafb085b4525aa","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"6d72a3eae97c6d721f3f523571319830ac2b9ff01780522d7edafb085b4525aa","first_computed_at":"2026-07-05T09:07:13.367797Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T09:07:13.367797Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"zGsy0Tm4ANVxXlWfEqVUBIHpSPEyMwadZGK0KiJ3UTzLzHhJkRCHZSQk/2QFcI/LdLeeywSbjf8w1r3EwheODQ==","signature_status":"signed_v1","signed_at":"2026-07-05T09:07:13.368232Z","signed_message":"canonical_sha256_bytes"},"source_id":"2409.09708","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:969e5952c7c2386db5dbdbe9bfc2b8660a387031fc81564959f01e6a2e2a3304","sha256:8c5719568deb7ba00eabfa01b746a81f8fccdc829193d8ba03e564db80c797c9"],"state_sha256":"cbcfcad21395040710c63969d01aea2b88e8fcc441aa728193da87fae038367c"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"3UFve7QQ+v5la1qs8KksncIVjPBhZ91UwpNh27z12Rp/pUdLvxOBPywHjqCuuI0fNMnptPthkowBVkxPkaUBDg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-06T20:24:50.092715Z","bundle_sha256":"9252d3beb3539f7f393a5c35e8e40d699dd08457299cd45f3dc398dbc8d0cfee"}}