{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:GB7GTPR74U2CW2FXMWLSG7CDSO","short_pith_number":"pith:GB7GTPR7","canonical_record":{"source":{"id":"1706.07853","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.DC","submitted_at":"2017-06-23T20:35:42Z","cross_cats_sorted":["cs.AR","cs.LG"],"title_canon_sha256":"5eb8a1a8347f8ffbf931be5fe032fac4688a8c65b7825e466e8818a103579399","abstract_canon_sha256":"bafb5202b537bb0e34a49088909719d9f06ab9ae31ea4acbd99c364c17357839"},"schema_version":"1.0"},"canonical_sha256":"307e69be3fe5342b68b76597237c43938b065a1d5c3ecd05f1562fbc5b43f477","source":{"kind":"arxiv","id":"1706.07853","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1706.07853","created_at":"2026-05-18T00:15:47Z"},{"alias_kind":"arxiv_version","alias_value":"1706.07853v2","created_at":"2026-05-18T00:15:47Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1706.07853","created_at":"2026-05-18T00:15:47Z"},{"alias_kind":"pith_short_12","alias_value":"GB7GTPR74U2C","created_at":"2026-05-18T12:31:15Z"},{"alias_kind":"pith_short_16","alias_value":"GB7GTPR74U2CW2FX","created_at":"2026-05-18T12:31:15Z"},{"alias_kind":"pith_short_8","alias_value":"GB7GTPR7","created_at":"2026-05-18T12:31:15Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:GB7GTPR74U2CW2FXMWLSG7CDSO","target":"record","payload":{"canonical_record":{"source":{"id":"1706.07853","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.DC","submitted_at":"2017-06-23T20:35:42Z","cross_cats_sorted":["cs.AR","cs.LG"],"title_canon_sha256":"5eb8a1a8347f8ffbf931be5fe032fac4688a8c65b7825e466e8818a103579399","abstract_canon_sha256":"bafb5202b537bb0e34a49088909719d9f06ab9ae31ea4acbd99c364c17357839"},"schema_version":"1.0"},"canonical_sha256":"307e69be3fe5342b68b76597237c43938b065a1d5c3ecd05f1562fbc5b43f477","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:15:47.230016Z","signature_b64":"3Tf60Oph5yqs63H0z31mtQJByLqoekxx1ZqCZtyYINYYUTWWOYbGyJqmQYwvIDQ2w6QVBOZ7BaYeHnYYUjPdBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"307e69be3fe5342b68b76597237c43938b065a1d5c3ecd05f1562fbc5b43f477","last_reissued_at":"2026-05-18T00:15:47.229376Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:15:47.229376Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1706.07853","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:15:47Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"OJdWJRg9e6nv2Be74ku6+kBb0qbc9TuNAzXDDu3qixgZE9UuK3kfMxHBnpKBPm5oCEjEr+cyN89/qDXvDT/ZCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-03T12:36:02.938707Z"},"content_sha256":"ed81ba7881afc7d30a6de706454c77ca3fb0e647e21cd136ad8aa6e4ee6c5b0a","schema_version":"1.0","event_id":"sha256:ed81ba7881afc7d30a6de706454c77ca3fb0e647e21cd136ad8aa6e4ee6c5b0a"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:GB7GTPR74U2CW2FXMWLSG7CDSO","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Loom: Exploiting Weight and Activation Precisions to Accelerate Convolutional Neural Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AR","cs.LG"],"primary_cat":"cs.DC","authors_text":"Alberto Delmas Lascorz, Andreas Moshovos, Kevin Siu, Patrick Judd, Sayeh Sharify","submitted_at":"2017-06-23T20:35:42Z","abstract_excerpt":"Loom (LM), a hardware inference accelerator for Convolutional Neural Networks (CNNs) is presented. In LM every bit of data precision that can be saved translates to proportional performance gains. Specifically, for convolutional layers LM's execution time scales inversely proportionally with the precisions of both weights and activations. For fully-connected layers LM's performance scales inversely proportionally with the precision of the weights. LM targets area- and bandwidth-constrained System-on-a-Chip designs such as those found on mobile devices that cannot afford the multi-megabyte buff"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1706.07853","kind":"arxiv","version":2},"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":"2026-05-18T00:15:47Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"HtIl6t9QRM6B/ZFhOaDDbmRKG6Ee2fNmyfzx0h0TPjxM1DksB2qhMEoX0TWsR48pW4OKpQfZgUEVgAL+7JcEDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-03T12:36:02.939356Z"},"content_sha256":"bf0499fbf453e0d7bf4291215e5a9941bf467dccb20d1357139f513725fbd068","schema_version":"1.0","event_id":"sha256:bf0499fbf453e0d7bf4291215e5a9941bf467dccb20d1357139f513725fbd068"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/GB7GTPR74U2CW2FXMWLSG7CDSO/bundle.json","state_url":"https://pith.science/pith/GB7GTPR74U2CW2FXMWLSG7CDSO/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/GB7GTPR74U2CW2FXMWLSG7CDSO/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":"2026-06-03T12:36:02Z","links":{"resolver":"https://pith.science/pith/GB7GTPR74U2CW2FXMWLSG7CDSO","bundle":"https://pith.science/pith/GB7GTPR74U2CW2FXMWLSG7CDSO/bundle.json","state":"https://pith.science/pith/GB7GTPR74U2CW2FXMWLSG7CDSO/state.json","well_known_bundle":"https://pith.science/.well-known/pith/GB7GTPR74U2CW2FXMWLSG7CDSO/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:GB7GTPR74U2CW2FXMWLSG7CDSO","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":"bafb5202b537bb0e34a49088909719d9f06ab9ae31ea4acbd99c364c17357839","cross_cats_sorted":["cs.AR","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.DC","submitted_at":"2017-06-23T20:35:42Z","title_canon_sha256":"5eb8a1a8347f8ffbf931be5fe032fac4688a8c65b7825e466e8818a103579399"},"schema_version":"1.0","source":{"id":"1706.07853","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1706.07853","created_at":"2026-05-18T00:15:47Z"},{"alias_kind":"arxiv_version","alias_value":"1706.07853v2","created_at":"2026-05-18T00:15:47Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1706.07853","created_at":"2026-05-18T00:15:47Z"},{"alias_kind":"pith_short_12","alias_value":"GB7GTPR74U2C","created_at":"2026-05-18T12:31:15Z"},{"alias_kind":"pith_short_16","alias_value":"GB7GTPR74U2CW2FX","created_at":"2026-05-18T12:31:15Z"},{"alias_kind":"pith_short_8","alias_value":"GB7GTPR7","created_at":"2026-05-18T12:31:15Z"}],"graph_snapshots":[{"event_id":"sha256:bf0499fbf453e0d7bf4291215e5a9941bf467dccb20d1357139f513725fbd068","target":"graph","created_at":"2026-05-18T00:15:47Z","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":"Loom (LM), a hardware inference accelerator for Convolutional Neural Networks (CNNs) is presented. In LM every bit of data precision that can be saved translates to proportional performance gains. Specifically, for convolutional layers LM's execution time scales inversely proportionally with the precisions of both weights and activations. For fully-connected layers LM's performance scales inversely proportionally with the precision of the weights. LM targets area- and bandwidth-constrained System-on-a-Chip designs such as those found on mobile devices that cannot afford the multi-megabyte buff","authors_text":"Alberto Delmas Lascorz, Andreas Moshovos, Kevin Siu, Patrick Judd, Sayeh Sharify","cross_cats":["cs.AR","cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.DC","submitted_at":"2017-06-23T20:35:42Z","title":"Loom: Exploiting Weight and Activation Precisions to Accelerate Convolutional Neural Networks"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1706.07853","kind":"arxiv","version":2},"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:ed81ba7881afc7d30a6de706454c77ca3fb0e647e21cd136ad8aa6e4ee6c5b0a","target":"record","created_at":"2026-05-18T00:15:47Z","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":"bafb5202b537bb0e34a49088909719d9f06ab9ae31ea4acbd99c364c17357839","cross_cats_sorted":["cs.AR","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.DC","submitted_at":"2017-06-23T20:35:42Z","title_canon_sha256":"5eb8a1a8347f8ffbf931be5fe032fac4688a8c65b7825e466e8818a103579399"},"schema_version":"1.0","source":{"id":"1706.07853","kind":"arxiv","version":2}},"canonical_sha256":"307e69be3fe5342b68b76597237c43938b065a1d5c3ecd05f1562fbc5b43f477","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"307e69be3fe5342b68b76597237c43938b065a1d5c3ecd05f1562fbc5b43f477","first_computed_at":"2026-05-18T00:15:47.229376Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:15:47.229376Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"3Tf60Oph5yqs63H0z31mtQJByLqoekxx1ZqCZtyYINYYUTWWOYbGyJqmQYwvIDQ2w6QVBOZ7BaYeHnYYUjPdBQ==","signature_status":"signed_v1","signed_at":"2026-05-18T00:15:47.230016Z","signed_message":"canonical_sha256_bytes"},"source_id":"1706.07853","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:ed81ba7881afc7d30a6de706454c77ca3fb0e647e21cd136ad8aa6e4ee6c5b0a","sha256:bf0499fbf453e0d7bf4291215e5a9941bf467dccb20d1357139f513725fbd068"],"state_sha256":"aaf7e6e789807ad18d59b92afe48d2aace3a64c2be8fabc7ddf6e26a4ed8e485"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"+IsLNBNcFUj5YfahRjuSySOVNGolQLInbWjXZBNPyBv8xrWSb/rqQJ2g4gyKJiSPFOrD8YNKqdqn6L1JJp38DQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-03T12:36:02.942284Z","bundle_sha256":"2ec810ba385966682fa632691c759405ce2400331cd01f5750ff3b907e5d18fc"}}