{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:E2L5WCFG7FEIPFVGGQ2NFUNARI","short_pith_number":"pith:E2L5WCFG","canonical_record":{"source":{"id":"1802.00822","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-02-02T19:26:59Z","cross_cats_sorted":["cs.AR","stat.ML"],"title_canon_sha256":"949dfb5b0bcceefff6895a0333d6212d168865c93a22870f722a6dacc410c13e","abstract_canon_sha256":"38686074ec5b2914eebbf8b7ff7f17a1408325ff9cedfe6b53e4ad557908ebc9"},"schema_version":"1.0"},"canonical_sha256":"2697db08a6f9488796a63434d2d1a08a33e7195c3f62048467dc709b70e3f615","source":{"kind":"arxiv","id":"1802.00822","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1802.00822","created_at":"2026-05-18T00:24:28Z"},{"alias_kind":"arxiv_version","alias_value":"1802.00822v1","created_at":"2026-05-18T00:24:28Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1802.00822","created_at":"2026-05-18T00:24:28Z"},{"alias_kind":"pith_short_12","alias_value":"E2L5WCFG7FEI","created_at":"2026-05-18T12:32:19Z"},{"alias_kind":"pith_short_16","alias_value":"E2L5WCFG7FEIPFVG","created_at":"2026-05-18T12:32:19Z"},{"alias_kind":"pith_short_8","alias_value":"E2L5WCFG","created_at":"2026-05-18T12:32:19Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:E2L5WCFG7FEIPFVGGQ2NFUNARI","target":"record","payload":{"canonical_record":{"source":{"id":"1802.00822","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-02-02T19:26:59Z","cross_cats_sorted":["cs.AR","stat.ML"],"title_canon_sha256":"949dfb5b0bcceefff6895a0333d6212d168865c93a22870f722a6dacc410c13e","abstract_canon_sha256":"38686074ec5b2914eebbf8b7ff7f17a1408325ff9cedfe6b53e4ad557908ebc9"},"schema_version":"1.0"},"canonical_sha256":"2697db08a6f9488796a63434d2d1a08a33e7195c3f62048467dc709b70e3f615","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:24:28.136500Z","signature_b64":"hHeimVmr+CM4XWjfw8LMaBu/yCZeGBPOneP8+SQgk6Cg/93+vK8mBfR3hVNDsIodBfPudnaKybA4PKR6KnvGCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2697db08a6f9488796a63434d2d1a08a33e7195c3f62048467dc709b70e3f615","last_reissued_at":"2026-05-18T00:24:28.136032Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:24:28.136032Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1802.00822","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":"2026-05-18T00:24:28Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"pBR9tc40GSyqoxe4CQionkhBknMnjmqARKbs3zhDHHpyiuuzh/XiLwZYsBMOth4zCR/d+U7Pq4+j52I2SmQTAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-27T08:51:46.730345Z"},"content_sha256":"6de872e4d5cef064decc03e5a82f90b6de3b0d13e6a522afec8406ca30c60eb0","schema_version":"1.0","event_id":"sha256:6de872e4d5cef064decc03e5a82f90b6de3b0d13e6a522afec8406ca30c60eb0"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:E2L5WCFG7FEIPFVGGQ2NFUNARI","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"VIBNN: Hardware Acceleration of Bayesian Neural Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AR","stat.ML"],"primary_cat":"cs.LG","authors_text":"Ao Ren, Caiwen Ding, Luhao Wang, Massoud Pedram, Ning Liu, Ruizhe Cai, Xuehai Qian, Yanzhi Wang","submitted_at":"2018-02-02T19:26:59Z","abstract_excerpt":"Bayesian Neural Networks (BNNs) have been proposed to address the problem of model uncertainty in training and inference. By introducing weights associated with conditioned probability distributions, BNNs are capable of resolving the overfitting issue commonly seen in conventional neural networks and allow for small-data training, through the variational inference process. Frequent usage of Gaussian random variables in this process requires a properly optimized Gaussian Random Number Generator (GRNG). The high hardware cost of conventional GRNG makes the hardware implementation of BNNs challen"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1802.00822","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":"2026-05-18T00:24:28Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"iSmiBCYnB+NQh4l6d1aCh05oecLAvDbHXz1BzCxloIKy2hoVJ8aoLdQ3GPQE2LP/L1aQCPsKXVZonqEoXnWHDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-27T08:51:46.730963Z"},"content_sha256":"5a8164419cff1fc70645c3ccac89202f9f2d202d0f3d9cbbdc663057cf673d5a","schema_version":"1.0","event_id":"sha256:5a8164419cff1fc70645c3ccac89202f9f2d202d0f3d9cbbdc663057cf673d5a"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/E2L5WCFG7FEIPFVGGQ2NFUNARI/bundle.json","state_url":"https://pith.science/pith/E2L5WCFG7FEIPFVGGQ2NFUNARI/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/E2L5WCFG7FEIPFVGGQ2NFUNARI/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":"2026-05-27T08:51:46Z","links":{"resolver":"https://pith.science/pith/E2L5WCFG7FEIPFVGGQ2NFUNARI","bundle":"https://pith.science/pith/E2L5WCFG7FEIPFVGGQ2NFUNARI/bundle.json","state":"https://pith.science/pith/E2L5WCFG7FEIPFVGGQ2NFUNARI/state.json","well_known_bundle":"https://pith.science/.well-known/pith/E2L5WCFG7FEIPFVGGQ2NFUNARI/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:E2L5WCFG7FEIPFVGGQ2NFUNARI","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":"38686074ec5b2914eebbf8b7ff7f17a1408325ff9cedfe6b53e4ad557908ebc9","cross_cats_sorted":["cs.AR","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-02-02T19:26:59Z","title_canon_sha256":"949dfb5b0bcceefff6895a0333d6212d168865c93a22870f722a6dacc410c13e"},"schema_version":"1.0","source":{"id":"1802.00822","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1802.00822","created_at":"2026-05-18T00:24:28Z"},{"alias_kind":"arxiv_version","alias_value":"1802.00822v1","created_at":"2026-05-18T00:24:28Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1802.00822","created_at":"2026-05-18T00:24:28Z"},{"alias_kind":"pith_short_12","alias_value":"E2L5WCFG7FEI","created_at":"2026-05-18T12:32:19Z"},{"alias_kind":"pith_short_16","alias_value":"E2L5WCFG7FEIPFVG","created_at":"2026-05-18T12:32:19Z"},{"alias_kind":"pith_short_8","alias_value":"E2L5WCFG","created_at":"2026-05-18T12:32:19Z"}],"graph_snapshots":[{"event_id":"sha256:5a8164419cff1fc70645c3ccac89202f9f2d202d0f3d9cbbdc663057cf673d5a","target":"graph","created_at":"2026-05-18T00:24:28Z","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":"Bayesian Neural Networks (BNNs) have been proposed to address the problem of model uncertainty in training and inference. By introducing weights associated with conditioned probability distributions, BNNs are capable of resolving the overfitting issue commonly seen in conventional neural networks and allow for small-data training, through the variational inference process. Frequent usage of Gaussian random variables in this process requires a properly optimized Gaussian Random Number Generator (GRNG). The high hardware cost of conventional GRNG makes the hardware implementation of BNNs challen","authors_text":"Ao Ren, Caiwen Ding, Luhao Wang, Massoud Pedram, Ning Liu, Ruizhe Cai, Xuehai Qian, Yanzhi Wang","cross_cats":["cs.AR","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-02-02T19:26:59Z","title":"VIBNN: Hardware Acceleration of Bayesian Neural Networks"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1802.00822","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":"sha256:6de872e4d5cef064decc03e5a82f90b6de3b0d13e6a522afec8406ca30c60eb0","target":"record","created_at":"2026-05-18T00:24:28Z","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":"38686074ec5b2914eebbf8b7ff7f17a1408325ff9cedfe6b53e4ad557908ebc9","cross_cats_sorted":["cs.AR","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-02-02T19:26:59Z","title_canon_sha256":"949dfb5b0bcceefff6895a0333d6212d168865c93a22870f722a6dacc410c13e"},"schema_version":"1.0","source":{"id":"1802.00822","kind":"arxiv","version":1}},"canonical_sha256":"2697db08a6f9488796a63434d2d1a08a33e7195c3f62048467dc709b70e3f615","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"2697db08a6f9488796a63434d2d1a08a33e7195c3f62048467dc709b70e3f615","first_computed_at":"2026-05-18T00:24:28.136032Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:24:28.136032Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"hHeimVmr+CM4XWjfw8LMaBu/yCZeGBPOneP8+SQgk6Cg/93+vK8mBfR3hVNDsIodBfPudnaKybA4PKR6KnvGCQ==","signature_status":"signed_v1","signed_at":"2026-05-18T00:24:28.136500Z","signed_message":"canonical_sha256_bytes"},"source_id":"1802.00822","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:6de872e4d5cef064decc03e5a82f90b6de3b0d13e6a522afec8406ca30c60eb0","sha256:5a8164419cff1fc70645c3ccac89202f9f2d202d0f3d9cbbdc663057cf673d5a"],"state_sha256":"e4d36d7b7559706ff56409bd3091498391a9b7d7b9808f708c8b4fff38fefafc"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"qvjQuhxEOlOq0fq2KxdUUI4IH7w0Dau6P6sArctZlAr1i/tasfKZrQVcGJ5HQ1fLpVxHFpRTxR84b3JrOxPlBA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-27T08:51:46.734514Z","bundle_sha256":"d45f02a4f273654faf3c6f30b48fcfd391e9147e6d705e74cd6c62e336c0742c"}}