{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:TWWCXSCQY3RVJLUQX3DTPLKTDO","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":"20cfc77384352b82310da969b94510485725056e50ffeb107ebdecff8c928bb7","cross_cats_sorted":["cs.CV"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-01-31T02:18:27Z","title_canon_sha256":"1f21b5100ba7acdb67a91e8e72d00d9a6d9568c3268bfd6b36acca303b6a0b88"},"schema_version":"1.0","source":{"id":"2401.17544","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2401.17544","created_at":"2026-07-05T07:39:38Z"},{"alias_kind":"arxiv_version","alias_value":"2401.17544v1","created_at":"2026-07-05T07:39:38Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2401.17544","created_at":"2026-07-05T07:39:38Z"},{"alias_kind":"pith_short_12","alias_value":"TWWCXSCQY3RV","created_at":"2026-07-05T07:39:38Z"},{"alias_kind":"pith_short_16","alias_value":"TWWCXSCQY3RVJLUQ","created_at":"2026-07-05T07:39:38Z"},{"alias_kind":"pith_short_8","alias_value":"TWWCXSCQ","created_at":"2026-07-05T07:39:38Z"}],"graph_snapshots":[{"event_id":"sha256:d3d601e470daf76b60a315fc5e41b4fbc5b2e8e03384294d29899b9ba6535bd4","target":"graph","created_at":"2026-07-05T07:39:38Z","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/2401.17544/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Quantization is a crucial technique for deploying deep learning models on resource-constrained devices, such as embedded FPGAs. Prior efforts mostly focus on quantizing matrix multiplications, leaving other layers like BatchNorm or shortcuts in floating-point form, even though fixed-point arithmetic is more efficient on FPGAs. A common practice is to fine-tune a pre-trained model to fixed-point for FPGA deployment, but potentially degrading accuracy.\n  This work presents QFX, a novel trainable fixed-point quantization approach that automatically learns the binary-point position during model tr","authors_text":"Dingyi Dai, Jiahao Zhang, Qi Sun, Yaohui Cai, Yichi Zhang, Zhanqiu Hu, Zhiru Zhang","cross_cats":["cs.CV"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-01-31T02:18:27Z","title":"Trainable Fixed-Point Quantization for Deep Learning Acceleration on FPGAs"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2401.17544","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":"sha256:806346ef4125e6509fd7f26622845f01033fc97a8084e8b4390db3d35df491ef","target":"record","created_at":"2026-07-05T07:39:38Z","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":"20cfc77384352b82310da969b94510485725056e50ffeb107ebdecff8c928bb7","cross_cats_sorted":["cs.CV"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-01-31T02:18:27Z","title_canon_sha256":"1f21b5100ba7acdb67a91e8e72d00d9a6d9568c3268bfd6b36acca303b6a0b88"},"schema_version":"1.0","source":{"id":"2401.17544","kind":"arxiv","version":1}},"canonical_sha256":"9dac2bc850c6e354ae90bec737ad531ba8e39366b20e08df60386259eb7322ba","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"9dac2bc850c6e354ae90bec737ad531ba8e39366b20e08df60386259eb7322ba","first_computed_at":"2026-07-05T07:39:38.428721Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T07:39:38.428721Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"mzTUbSY7llh69P+oIMo5STP2iBvBjDbt1hL6jEjSAO4vMqEQ7hTXjL2cOezqMCRDwzI1d2s8BECeEUOjqkcEAQ==","signature_status":"signed_v1","signed_at":"2026-07-05T07:39:38.429156Z","signed_message":"canonical_sha256_bytes"},"source_id":"2401.17544","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:806346ef4125e6509fd7f26622845f01033fc97a8084e8b4390db3d35df491ef","sha256:d3d601e470daf76b60a315fc5e41b4fbc5b2e8e03384294d29899b9ba6535bd4"],"state_sha256":"b843eef3394d56ee58dd94a93328d2568d711ad89ee9e3a21a3c4921d860f3f5"}