{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:HFCX6I3A6FWY7BJHYQNDWH4BJB","short_pith_number":"pith:HFCX6I3A","canonical_record":{"source":{"id":"1804.06508","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NE","submitted_at":"2018-04-18T00:11:38Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"96e9c1b5a8c9bbf3680fee08c02facab2af43d232d61abf7957bf6ea326ae308","abstract_canon_sha256":"7174d7918adba9df2d7a68e9ec0d2d8049891c56d837a7fe37680125c9f2fae2"},"schema_version":"1.0"},"canonical_sha256":"39457f2360f16d8f8527c41a3b1f814875a1e5e1db89c0b426a83c28077b4b05","source":{"kind":"arxiv","id":"1804.06508","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1804.06508","created_at":"2026-05-18T00:18:06Z"},{"alias_kind":"arxiv_version","alias_value":"1804.06508v1","created_at":"2026-05-18T00:18:06Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1804.06508","created_at":"2026-05-18T00:18:06Z"},{"alias_kind":"pith_short_12","alias_value":"HFCX6I3A6FWY","created_at":"2026-05-18T12:32:28Z"},{"alias_kind":"pith_short_16","alias_value":"HFCX6I3A6FWY7BJH","created_at":"2026-05-18T12:32:28Z"},{"alias_kind":"pith_short_8","alias_value":"HFCX6I3A","created_at":"2026-05-18T12:32:28Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:HFCX6I3A6FWY7BJHYQNDWH4BJB","target":"record","payload":{"canonical_record":{"source":{"id":"1804.06508","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NE","submitted_at":"2018-04-18T00:11:38Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"96e9c1b5a8c9bbf3680fee08c02facab2af43d232d61abf7957bf6ea326ae308","abstract_canon_sha256":"7174d7918adba9df2d7a68e9ec0d2d8049891c56d837a7fe37680125c9f2fae2"},"schema_version":"1.0"},"canonical_sha256":"39457f2360f16d8f8527c41a3b1f814875a1e5e1db89c0b426a83c28077b4b05","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:18:06.450639Z","signature_b64":"Iv6zuIGN7HeIVggxGJIh6Tj5s0pws2u7gOzwxpo3qFDvbDqvNN/XBBQCkbIQYynMm5E7ZVoV07SVn7ta7N0VDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"39457f2360f16d8f8527c41a3b1f814875a1e5e1db89c0b426a83c28077b4b05","last_reissued_at":"2026-05-18T00:18:06.450007Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:18:06.450007Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1804.06508","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":"2026-05-18T00:18:06Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Vr69OLaT5Q6BvtlqXxLrITRSlRrb0LwE35UuQ9SNLJ9gSvLjnCBpzy7UaLil6+xi9og6vQACP+Ma089pHKyeCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-27T04:11:34.973989Z"},"content_sha256":"08f90cbd9c8fb6bdfc78c6d23f245d603896c33455ffd142ae87ba62acee828a","schema_version":"1.0","event_id":"sha256:08f90cbd9c8fb6bdfc78c6d23f245d603896c33455ffd142ae87ba62acee828a"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:HFCX6I3A6FWY7BJHYQNDWH4BJB","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"UCNN: Exploiting Computational Reuse in Deep Neural Networks via Weight Repetition","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.NE","authors_text":"Christopher W. Fletcher, Jiyong Yu, Kartik Hegde, Mengjia Yan, Michael Pellauer, Rohit Agrawal","submitted_at":"2018-04-18T00:11:38Z","abstract_excerpt":"Convolutional Neural Networks (CNNs) have begun to permeate all corners of electronic society (from voice recognition to scene generation) due to their high accuracy and machine efficiency per operation. At their core, CNN computations are made up of multi-dimensional dot products between weight and input vectors. This paper studies how weight repetition ---when the same weight occurs multiple times in or across weight vectors--- can be exploited to save energy and improve performance during CNN inference. This generalizes a popular line of work to improve efficiency from CNN weight sparsity, "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1804.06508","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":"2026-05-18T00:18:06Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"SaQBCG3ZX5C0xYgvNcVIdx4DvOxWkyollDNN2SFcW/lj7oxLiiehdWaa/bLGhZoyZdyPEbzMvIuD3Lh7oa6vAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-27T04:11:34.974654Z"},"content_sha256":"310353cfca018af65265330d05afbb0f0b786cd6c9ab40d817b5cb4b70b810e3","schema_version":"1.0","event_id":"sha256:310353cfca018af65265330d05afbb0f0b786cd6c9ab40d817b5cb4b70b810e3"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/HFCX6I3A6FWY7BJHYQNDWH4BJB/bundle.json","state_url":"https://pith.science/pith/HFCX6I3A6FWY7BJHYQNDWH4BJB/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/HFCX6I3A6FWY7BJHYQNDWH4BJB/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":"2026-05-27T04:11:34Z","links":{"resolver":"https://pith.science/pith/HFCX6I3A6FWY7BJHYQNDWH4BJB","bundle":"https://pith.science/pith/HFCX6I3A6FWY7BJHYQNDWH4BJB/bundle.json","state":"https://pith.science/pith/HFCX6I3A6FWY7BJHYQNDWH4BJB/state.json","well_known_bundle":"https://pith.science/.well-known/pith/HFCX6I3A6FWY7BJHYQNDWH4BJB/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:HFCX6I3A6FWY7BJHYQNDWH4BJB","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":"7174d7918adba9df2d7a68e9ec0d2d8049891c56d837a7fe37680125c9f2fae2","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NE","submitted_at":"2018-04-18T00:11:38Z","title_canon_sha256":"96e9c1b5a8c9bbf3680fee08c02facab2af43d232d61abf7957bf6ea326ae308"},"schema_version":"1.0","source":{"id":"1804.06508","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1804.06508","created_at":"2026-05-18T00:18:06Z"},{"alias_kind":"arxiv_version","alias_value":"1804.06508v1","created_at":"2026-05-18T00:18:06Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1804.06508","created_at":"2026-05-18T00:18:06Z"},{"alias_kind":"pith_short_12","alias_value":"HFCX6I3A6FWY","created_at":"2026-05-18T12:32:28Z"},{"alias_kind":"pith_short_16","alias_value":"HFCX6I3A6FWY7BJH","created_at":"2026-05-18T12:32:28Z"},{"alias_kind":"pith_short_8","alias_value":"HFCX6I3A","created_at":"2026-05-18T12:32:28Z"}],"graph_snapshots":[{"event_id":"sha256:310353cfca018af65265330d05afbb0f0b786cd6c9ab40d817b5cb4b70b810e3","target":"graph","created_at":"2026-05-18T00:18:06Z","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":"Convolutional Neural Networks (CNNs) have begun to permeate all corners of electronic society (from voice recognition to scene generation) due to their high accuracy and machine efficiency per operation. At their core, CNN computations are made up of multi-dimensional dot products between weight and input vectors. This paper studies how weight repetition ---when the same weight occurs multiple times in or across weight vectors--- can be exploited to save energy and improve performance during CNN inference. This generalizes a popular line of work to improve efficiency from CNN weight sparsity, ","authors_text":"Christopher W. Fletcher, Jiyong Yu, Kartik Hegde, Mengjia Yan, Michael Pellauer, Rohit Agrawal","cross_cats":["cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NE","submitted_at":"2018-04-18T00:11:38Z","title":"UCNN: Exploiting Computational Reuse in Deep Neural Networks via Weight Repetition"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1804.06508","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":"sha256:08f90cbd9c8fb6bdfc78c6d23f245d603896c33455ffd142ae87ba62acee828a","target":"record","created_at":"2026-05-18T00:18:06Z","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":"7174d7918adba9df2d7a68e9ec0d2d8049891c56d837a7fe37680125c9f2fae2","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NE","submitted_at":"2018-04-18T00:11:38Z","title_canon_sha256":"96e9c1b5a8c9bbf3680fee08c02facab2af43d232d61abf7957bf6ea326ae308"},"schema_version":"1.0","source":{"id":"1804.06508","kind":"arxiv","version":1}},"canonical_sha256":"39457f2360f16d8f8527c41a3b1f814875a1e5e1db89c0b426a83c28077b4b05","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"39457f2360f16d8f8527c41a3b1f814875a1e5e1db89c0b426a83c28077b4b05","first_computed_at":"2026-05-18T00:18:06.450007Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:18:06.450007Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Iv6zuIGN7HeIVggxGJIh6Tj5s0pws2u7gOzwxpo3qFDvbDqvNN/XBBQCkbIQYynMm5E7ZVoV07SVn7ta7N0VDQ==","signature_status":"signed_v1","signed_at":"2026-05-18T00:18:06.450639Z","signed_message":"canonical_sha256_bytes"},"source_id":"1804.06508","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:08f90cbd9c8fb6bdfc78c6d23f245d603896c33455ffd142ae87ba62acee828a","sha256:310353cfca018af65265330d05afbb0f0b786cd6c9ab40d817b5cb4b70b810e3"],"state_sha256":"65e9516dc8e67149a3e0ffd1b867df3b5418029b3a6203895dbd49c73dff35a2"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ITpodXjRmYsnX6q7ZLNHVpsN96fRhhcTkPqgDwyM6nadL6Iyvd5mIEr2EzU0qjhs0qb/npd7uWUASTDVxI0EBQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-27T04:11:34.978185Z","bundle_sha256":"5b852fb58d8f8cb4e2206aba4627446ee369a249e8bf3784a46cec8d8823f961"}}