{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:M6IXSCGDNDAM356RZGSM5TXJEN","short_pith_number":"pith:M6IXSCGD","canonical_record":{"source":{"id":"1809.06970","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-09-19T00:43:26Z","cross_cats_sorted":["cs.NI","cs.PF","cs.SY","stat.ML"],"title_canon_sha256":"d85c5ea83333bb82a0e493ea1cc9f591f8bc4bf35df74b5a48c24bfb8cc2c657","abstract_canon_sha256":"04f1ca4250ded6413a7556a494fc0144f94ec313ac6710e515e544f71af2d7a3"},"schema_version":"1.0"},"canonical_sha256":"67917908c368c0cdf7d1c9a4cecee92346223f23bf0812c515dbbe219405f18f","source":{"kind":"arxiv","id":"1809.06970","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1809.06970","created_at":"2026-05-18T00:05:21Z"},{"alias_kind":"arxiv_version","alias_value":"1809.06970v1","created_at":"2026-05-18T00:05:21Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1809.06970","created_at":"2026-05-18T00:05:21Z"},{"alias_kind":"pith_short_12","alias_value":"M6IXSCGDNDAM","created_at":"2026-05-18T12:32:37Z"},{"alias_kind":"pith_short_16","alias_value":"M6IXSCGDNDAM356R","created_at":"2026-05-18T12:32:37Z"},{"alias_kind":"pith_short_8","alias_value":"M6IXSCGD","created_at":"2026-05-18T12:32:37Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:M6IXSCGDNDAM356RZGSM5TXJEN","target":"record","payload":{"canonical_record":{"source":{"id":"1809.06970","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-09-19T00:43:26Z","cross_cats_sorted":["cs.NI","cs.PF","cs.SY","stat.ML"],"title_canon_sha256":"d85c5ea83333bb82a0e493ea1cc9f591f8bc4bf35df74b5a48c24bfb8cc2c657","abstract_canon_sha256":"04f1ca4250ded6413a7556a494fc0144f94ec313ac6710e515e544f71af2d7a3"},"schema_version":"1.0"},"canonical_sha256":"67917908c368c0cdf7d1c9a4cecee92346223f23bf0812c515dbbe219405f18f","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:05:21.032730Z","signature_b64":"87tCfU4N6fRa1RdfS3+1GF162wJa1uGGlMbRoE8eJbDuD/MYP38qYFoQOLjCqwP/111wAyvXMQ9NmZhTBcbVBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"67917908c368c0cdf7d1c9a4cecee92346223f23bf0812c515dbbe219405f18f","last_reissued_at":"2026-05-18T00:05:21.032213Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:05:21.032213Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1809.06970","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":"2026-05-18T00:05:21Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"+zLu74+/ypeZlh58/no6TO+G4wsKN/r80Ps2Fi06f8kcb+9PalSByZScg94nZajinBLNJ3kAEJjiWP7BxH9VDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-06T20:18:39.378307Z"},"content_sha256":"19aa7118cef84a291c4411b2c402035369f5fb9632111e3040e8a6c0c5c886b9","schema_version":"1.0","event_id":"sha256:19aa7118cef84a291c4411b2c402035369f5fb9632111e3040e8a6c0c5c886b9"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:M6IXSCGDNDAM356RZGSM5TXJEN","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"FastDeepIoT: Towards Understanding and Optimizing Neural Network Execution Time on Mobile and Embedded Devices","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.NI","cs.PF","cs.SY","stat.ML"],"primary_cat":"cs.LG","authors_text":"Dongxin Liu, Huajie Shao, Lu Su, Shengzhong Liu, Shuochao Yao, Tarek Abdelzaher, Yiran Zhao","submitted_at":"2018-09-19T00:43:26Z","abstract_excerpt":"Deep neural networks show great potential as solutions to many sensing application problems, but their excessive resource demand slows down execution time, pausing a serious impediment to deployment on low-end devices. To address this challenge, recent literature focused on compressing neural network size to improve performance. We show that changing neural network size does not proportionally affect performance attributes of interest, such as execution time. Rather, extreme run-time nonlinearities exist over the network configuration space. Hence, we propose a novel framework, called FastDeep"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1809.06970","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":""},"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":"2026-05-18T00:05:21Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"gOBfAFSuarGB6orbsDII1hGP4QUMJaSPhI0fHsXYsz2keEmYrQvMQPBC/RuDXE7yKkXTYOjuYHomyg6CfkW1BQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-06T20:18:39.378672Z"},"content_sha256":"cac2c9d4a74655df005bdc56f084b008b7cbd0bb9298503a6a25ba9a6efcdd2c","schema_version":"1.0","event_id":"sha256:cac2c9d4a74655df005bdc56f084b008b7cbd0bb9298503a6a25ba9a6efcdd2c"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/M6IXSCGDNDAM356RZGSM5TXJEN/bundle.json","state_url":"https://pith.science/pith/M6IXSCGDNDAM356RZGSM5TXJEN/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/M6IXSCGDNDAM356RZGSM5TXJEN/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":"2026-07-06T20:18:39Z","links":{"resolver":"https://pith.science/pith/M6IXSCGDNDAM356RZGSM5TXJEN","bundle":"https://pith.science/pith/M6IXSCGDNDAM356RZGSM5TXJEN/bundle.json","state":"https://pith.science/pith/M6IXSCGDNDAM356RZGSM5TXJEN/state.json","well_known_bundle":"https://pith.science/.well-known/pith/M6IXSCGDNDAM356RZGSM5TXJEN/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:M6IXSCGDNDAM356RZGSM5TXJEN","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":"04f1ca4250ded6413a7556a494fc0144f94ec313ac6710e515e544f71af2d7a3","cross_cats_sorted":["cs.NI","cs.PF","cs.SY","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-09-19T00:43:26Z","title_canon_sha256":"d85c5ea83333bb82a0e493ea1cc9f591f8bc4bf35df74b5a48c24bfb8cc2c657"},"schema_version":"1.0","source":{"id":"1809.06970","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1809.06970","created_at":"2026-05-18T00:05:21Z"},{"alias_kind":"arxiv_version","alias_value":"1809.06970v1","created_at":"2026-05-18T00:05:21Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1809.06970","created_at":"2026-05-18T00:05:21Z"},{"alias_kind":"pith_short_12","alias_value":"M6IXSCGDNDAM","created_at":"2026-05-18T12:32:37Z"},{"alias_kind":"pith_short_16","alias_value":"M6IXSCGDNDAM356R","created_at":"2026-05-18T12:32:37Z"},{"alias_kind":"pith_short_8","alias_value":"M6IXSCGD","created_at":"2026-05-18T12:32:37Z"}],"graph_snapshots":[{"event_id":"sha256:cac2c9d4a74655df005bdc56f084b008b7cbd0bb9298503a6a25ba9a6efcdd2c","target":"graph","created_at":"2026-05-18T00:05:21Z","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"},"paper":{"abstract_excerpt":"Deep neural networks show great potential as solutions to many sensing application problems, but their excessive resource demand slows down execution time, pausing a serious impediment to deployment on low-end devices. To address this challenge, recent literature focused on compressing neural network size to improve performance. We show that changing neural network size does not proportionally affect performance attributes of interest, such as execution time. Rather, extreme run-time nonlinearities exist over the network configuration space. Hence, we propose a novel framework, called FastDeep","authors_text":"Dongxin Liu, Huajie Shao, Lu Su, Shengzhong Liu, Shuochao Yao, Tarek Abdelzaher, Yiran Zhao","cross_cats":["cs.NI","cs.PF","cs.SY","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-09-19T00:43:26Z","title":"FastDeepIoT: Towards Understanding and Optimizing Neural Network Execution Time on Mobile and Embedded Devices"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1809.06970","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":"sha256:19aa7118cef84a291c4411b2c402035369f5fb9632111e3040e8a6c0c5c886b9","target":"record","created_at":"2026-05-18T00:05:21Z","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":"04f1ca4250ded6413a7556a494fc0144f94ec313ac6710e515e544f71af2d7a3","cross_cats_sorted":["cs.NI","cs.PF","cs.SY","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-09-19T00:43:26Z","title_canon_sha256":"d85c5ea83333bb82a0e493ea1cc9f591f8bc4bf35df74b5a48c24bfb8cc2c657"},"schema_version":"1.0","source":{"id":"1809.06970","kind":"arxiv","version":1}},"canonical_sha256":"67917908c368c0cdf7d1c9a4cecee92346223f23bf0812c515dbbe219405f18f","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"67917908c368c0cdf7d1c9a4cecee92346223f23bf0812c515dbbe219405f18f","first_computed_at":"2026-05-18T00:05:21.032213Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:05:21.032213Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"87tCfU4N6fRa1RdfS3+1GF162wJa1uGGlMbRoE8eJbDuD/MYP38qYFoQOLjCqwP/111wAyvXMQ9NmZhTBcbVBQ==","signature_status":"signed_v1","signed_at":"2026-05-18T00:05:21.032730Z","signed_message":"canonical_sha256_bytes"},"source_id":"1809.06970","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:19aa7118cef84a291c4411b2c402035369f5fb9632111e3040e8a6c0c5c886b9","sha256:cac2c9d4a74655df005bdc56f084b008b7cbd0bb9298503a6a25ba9a6efcdd2c"],"state_sha256":"5d66d609661e7b4dc3c4a6c3c8be1c4356730cf595749d6dd3f8566774fbbf74"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"iPKxEylLUe+yUxzOlnvYrYaexQXT/sMKBx5kyCZ4FsAhjvNqMXAGcQOhCY1aX8W6AU2C7TH+wdDA1zJZxRVmBg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-06T20:18:39.380638Z","bundle_sha256":"ce8ac110dab01e3519bd3072baf676abe8cac2a3053ecfd99b22aef347acc583"}}