{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2020:FIJBFCC7XDAYDDEV6XYC4EUWFM","short_pith_number":"pith:FIJBFCC7","canonical_record":{"source":{"id":"2006.10226","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.DC","submitted_at":"2020-06-18T01:38:10Z","cross_cats_sorted":["cs.LG","cs.PL"],"title_canon_sha256":"5391b5b5d2b90b0536108fb10133994a67e4158d8cdcc74d241d77ad68228ffb","abstract_canon_sha256":"b111f2b75b2d42825a7f1f68f4ecb89e9dccbb3b49ceb45dc515e5a247042c66"},"schema_version":"1.0"},"canonical_sha256":"2a1212885fb8c1818c95f5f02e12962b3fc6ca01a71a1fc7258e970be0d14728","source":{"kind":"arxiv","id":"2006.10226","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2006.10226","created_at":"2026-07-05T01:11:16Z"},{"alias_kind":"arxiv_version","alias_value":"2006.10226v1","created_at":"2026-07-05T01:11:16Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2006.10226","created_at":"2026-07-05T01:11:16Z"},{"alias_kind":"pith_short_12","alias_value":"FIJBFCC7XDAY","created_at":"2026-07-05T01:11:16Z"},{"alias_kind":"pith_short_16","alias_value":"FIJBFCC7XDAYDDEV","created_at":"2026-07-05T01:11:16Z"},{"alias_kind":"pith_short_8","alias_value":"FIJBFCC7","created_at":"2026-07-05T01:11:16Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2020:FIJBFCC7XDAYDDEV6XYC4EUWFM","target":"record","payload":{"canonical_record":{"source":{"id":"2006.10226","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.DC","submitted_at":"2020-06-18T01:38:10Z","cross_cats_sorted":["cs.LG","cs.PL"],"title_canon_sha256":"5391b5b5d2b90b0536108fb10133994a67e4158d8cdcc74d241d77ad68228ffb","abstract_canon_sha256":"b111f2b75b2d42825a7f1f68f4ecb89e9dccbb3b49ceb45dc515e5a247042c66"},"schema_version":"1.0"},"canonical_sha256":"2a1212885fb8c1818c95f5f02e12962b3fc6ca01a71a1fc7258e970be0d14728","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:11:16.112925Z","signature_b64":"lLEQxJ82coSpPgU2neDlW9Wd1MzZ3CSCtGCQ4J7w1M9RBhgVRvBVSdt2tyJ24Kz9Fdi6ML1e4WKI5LMV0lufAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2a1212885fb8c1818c95f5f02e12962b3fc6ca01a71a1fc7258e970be0d14728","last_reissued_at":"2026-07-05T01:11:16.112422Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:11:16.112422Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2006.10226","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":"2026-07-05T01:11:16Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"+pxhdaNudqh9oA2LdAVxK8I9/L2DzlNnkKjBQqXmGvxlDBTfzL7LPbBIW/61FVvb7EbO5Iot6WwMMmqAbV/ACg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-20T10:10:12.002928Z"},"content_sha256":"73b28ee5bfc87c4c66b43fe0dc1fd518f53d906ce25eea7fdbcab81eb5741e1c","schema_version":"1.0","event_id":"sha256:73b28ee5bfc87c4c66b43fe0dc1fd518f53d906ce25eea7fdbcab81eb5741e1c"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2020:FIJBFCC7XDAYDDEV6XYC4EUWFM","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Efficient Execution of Quantized Deep Learning Models: A Compiler Approach","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","cs.PL"],"primary_cat":"cs.DC","authors_text":"Animesh Jain, Masahiro Masuda, Shoubhik Bhattacharya, Vin Sharma, Yida Wang","submitted_at":"2020-06-18T01:38:10Z","abstract_excerpt":"A growing number of applications implement predictive functions using deep learning models, which require heavy use of compute and memory. One popular technique for increasing resource efficiency is 8-bit integer quantization, in which 32-bit floating point numbers (fp32) are represented using shorter 8-bit integer numbers. Although deep learning frameworks such as TensorFlow, TFLite, MXNet, and PyTorch enable developers to quantize models with only a small drop in accuracy, they are not well suited to execute quantized models on a variety of hardware platforms. For example, TFLite is optimize"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2006.10226","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/2006.10226/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":"2026-07-05T01:11:16Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ipcUfk7e+fkXO8AtFKXB35nXH0c0d8TAbAnjVE0y4e/cdMhXKf7B05vBlOEPiaQcQizROHQfi+qqN9+RWx+vCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-20T10:10:12.003436Z"},"content_sha256":"b9b3cd25279a26aff7a52c14b3b6c5f070ce6217dc8c299caccaab5ef56deb8d","schema_version":"1.0","event_id":"sha256:b9b3cd25279a26aff7a52c14b3b6c5f070ce6217dc8c299caccaab5ef56deb8d"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/FIJBFCC7XDAYDDEV6XYC4EUWFM/bundle.json","state_url":"https://pith.science/pith/FIJBFCC7XDAYDDEV6XYC4EUWFM/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/FIJBFCC7XDAYDDEV6XYC4EUWFM/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":"2026-08-20T10:10:12Z","links":{"resolver":"https://pith.science/pith/FIJBFCC7XDAYDDEV6XYC4EUWFM","bundle":"https://pith.science/pith/FIJBFCC7XDAYDDEV6XYC4EUWFM/bundle.json","state":"https://pith.science/pith/FIJBFCC7XDAYDDEV6XYC4EUWFM/state.json","well_known_bundle":"https://pith.science/.well-known/pith/FIJBFCC7XDAYDDEV6XYC4EUWFM/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2020:FIJBFCC7XDAYDDEV6XYC4EUWFM","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":"b111f2b75b2d42825a7f1f68f4ecb89e9dccbb3b49ceb45dc515e5a247042c66","cross_cats_sorted":["cs.LG","cs.PL"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.DC","submitted_at":"2020-06-18T01:38:10Z","title_canon_sha256":"5391b5b5d2b90b0536108fb10133994a67e4158d8cdcc74d241d77ad68228ffb"},"schema_version":"1.0","source":{"id":"2006.10226","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2006.10226","created_at":"2026-07-05T01:11:16Z"},{"alias_kind":"arxiv_version","alias_value":"2006.10226v1","created_at":"2026-07-05T01:11:16Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2006.10226","created_at":"2026-07-05T01:11:16Z"},{"alias_kind":"pith_short_12","alias_value":"FIJBFCC7XDAY","created_at":"2026-07-05T01:11:16Z"},{"alias_kind":"pith_short_16","alias_value":"FIJBFCC7XDAYDDEV","created_at":"2026-07-05T01:11:16Z"},{"alias_kind":"pith_short_8","alias_value":"FIJBFCC7","created_at":"2026-07-05T01:11:16Z"}],"graph_snapshots":[{"event_id":"sha256:b9b3cd25279a26aff7a52c14b3b6c5f070ce6217dc8c299caccaab5ef56deb8d","target":"graph","created_at":"2026-07-05T01:11:16Z","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/2006.10226/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"A growing number of applications implement predictive functions using deep learning models, which require heavy use of compute and memory. One popular technique for increasing resource efficiency is 8-bit integer quantization, in which 32-bit floating point numbers (fp32) are represented using shorter 8-bit integer numbers. Although deep learning frameworks such as TensorFlow, TFLite, MXNet, and PyTorch enable developers to quantize models with only a small drop in accuracy, they are not well suited to execute quantized models on a variety of hardware platforms. For example, TFLite is optimize","authors_text":"Animesh Jain, Masahiro Masuda, Shoubhik Bhattacharya, Vin Sharma, Yida Wang","cross_cats":["cs.LG","cs.PL"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.DC","submitted_at":"2020-06-18T01:38:10Z","title":"Efficient Execution of Quantized Deep Learning Models: A Compiler Approach"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2006.10226","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":"sha256:73b28ee5bfc87c4c66b43fe0dc1fd518f53d906ce25eea7fdbcab81eb5741e1c","target":"record","created_at":"2026-07-05T01:11:16Z","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":"b111f2b75b2d42825a7f1f68f4ecb89e9dccbb3b49ceb45dc515e5a247042c66","cross_cats_sorted":["cs.LG","cs.PL"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.DC","submitted_at":"2020-06-18T01:38:10Z","title_canon_sha256":"5391b5b5d2b90b0536108fb10133994a67e4158d8cdcc74d241d77ad68228ffb"},"schema_version":"1.0","source":{"id":"2006.10226","kind":"arxiv","version":1}},"canonical_sha256":"2a1212885fb8c1818c95f5f02e12962b3fc6ca01a71a1fc7258e970be0d14728","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"2a1212885fb8c1818c95f5f02e12962b3fc6ca01a71a1fc7258e970be0d14728","first_computed_at":"2026-07-05T01:11:16.112422Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T01:11:16.112422Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"lLEQxJ82coSpPgU2neDlW9Wd1MzZ3CSCtGCQ4J7w1M9RBhgVRvBVSdt2tyJ24Kz9Fdi6ML1e4WKI5LMV0lufAQ==","signature_status":"signed_v1","signed_at":"2026-07-05T01:11:16.112925Z","signed_message":"canonical_sha256_bytes"},"source_id":"2006.10226","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:73b28ee5bfc87c4c66b43fe0dc1fd518f53d906ce25eea7fdbcab81eb5741e1c","sha256:b9b3cd25279a26aff7a52c14b3b6c5f070ce6217dc8c299caccaab5ef56deb8d"],"state_sha256":"5d1bd7702a0d4fe7292b249d95c4b047c9e150b8a1ae806c187e10726342c020"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"+PVf9UCkT3GGWoqDpm4vC/8hAK5zw85AUKgfK8VmY0HxKHTDmlAQnnyuNDhYgcGx46y4GZn8ZmMRCH7FhVFeCQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-20T10:10:12.007205Z","bundle_sha256":"3a32486ccadc62e45ef8a1a1ed50df5b19ea96a585db6a0d77c4601bd9beec3b"}}