{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:WMFG67MAXRJRKOKMHEGBDPZQNG","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":"9fcebe7b1dc67bdb70d313e48cdaa306147a44d7c47361285e252c396d3cc6d3","cross_cats_sorted":["cs.DC","eess.IV","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-05-21T22:27:14Z","title_canon_sha256":"7962ebcf806131b66cf318b9ca4bf26ddadbcd3e48bc8fbc01f9007421ef0f34"},"schema_version":"1.0","source":{"id":"1805.08309","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1805.08309","created_at":"2026-05-18T00:15:26Z"},{"alias_kind":"arxiv_version","alias_value":"1805.08309v1","created_at":"2026-05-18T00:15:26Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1805.08309","created_at":"2026-05-18T00:15:26Z"},{"alias_kind":"pith_short_12","alias_value":"WMFG67MAXRJR","created_at":"2026-05-18T12:33:01Z"},{"alias_kind":"pith_short_16","alias_value":"WMFG67MAXRJRKOKM","created_at":"2026-05-18T12:33:01Z"},{"alias_kind":"pith_short_8","alias_value":"WMFG67MA","created_at":"2026-05-18T12:33:01Z"}],"graph_snapshots":[{"event_id":"sha256:2a65599b9bc9918196e1ea058797573bbb74728aad1495c3103622c51650712f","target":"graph","created_at":"2026-05-18T00:15:26Z","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":"The intrinsic error tolerance of neural network (NN) makes approximate computing a promising technique to improve the energy efficiency of NN inference. Conventional approximate computing focuses on balancing the efficiency-accuracy trade-off for existing pre-trained networks, which can lead to suboptimal solutions. In this paper, we propose AxTrain, a hardware-oriented training framework to facilitate approximate computing for NN inference. Specifically, AxTrain leverages the synergy between two orthogonal methods---one actively searches for a network parameters distribution with high error t","authors_text":"Guihai Yan, Liu Ke, Wenyan Lu, Xin He, Xuan Zhang","cross_cats":["cs.DC","eess.IV","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-05-21T22:27:14Z","title":"AxTrain: Hardware-Oriented Neural Network Training for Approximate Inference"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1805.08309","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":"sha256:2d4162ea9692302f50860602530d919ec15b3403d8218a4d8817d3b7d65e4cd6","target":"record","created_at":"2026-05-18T00:15:26Z","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":"9fcebe7b1dc67bdb70d313e48cdaa306147a44d7c47361285e252c396d3cc6d3","cross_cats_sorted":["cs.DC","eess.IV","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-05-21T22:27:14Z","title_canon_sha256":"7962ebcf806131b66cf318b9ca4bf26ddadbcd3e48bc8fbc01f9007421ef0f34"},"schema_version":"1.0","source":{"id":"1805.08309","kind":"arxiv","version":1}},"canonical_sha256":"b30a6f7d80bc5315394c390c11bf3069bb0fda107c756d42817b35fdd8bf9fa5","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"b30a6f7d80bc5315394c390c11bf3069bb0fda107c756d42817b35fdd8bf9fa5","first_computed_at":"2026-05-18T00:15:26.959856Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:15:26.959856Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"6iJ5mEV0ZOcyEm1pRRVbll0NsKh5fo15RunfD7Yi3vmy2xwW8qOzci/8EeQ6WPYEftqpv2mGO9MJOo+pQQs3BQ==","signature_status":"signed_v1","signed_at":"2026-05-18T00:15:26.960687Z","signed_message":"canonical_sha256_bytes"},"source_id":"1805.08309","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:2d4162ea9692302f50860602530d919ec15b3403d8218a4d8817d3b7d65e4cd6","sha256:2a65599b9bc9918196e1ea058797573bbb74728aad1495c3103622c51650712f"],"state_sha256":"63e28fb3217b7e0fd48200cabc0ce4e3541a95c8a9b55386d0a82d84728f2bd4"}