{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:U2JW2GOE4LRRHY5QJJVKOC5BCW","short_pith_number":"pith:U2JW2GOE","canonical_record":{"source":{"id":"2506.09282","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.DC","submitted_at":"2025-06-10T22:46:12Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"361f44777d92608fee3540a13875f092aebc5d6c7e2daeebc13600945ae65bd1","abstract_canon_sha256":"d0101c991f921a8dbd8054d58220dbf27046073d5921be0c1c5fc6ce63820627"},"schema_version":"1.0"},"canonical_sha256":"a6936d19c4e2e313e3b04a6aa70ba115932030bd1d10ae9c6ccc7e55600957ab","source":{"kind":"arxiv","id":"2506.09282","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2506.09282","created_at":"2026-07-05T11:19:34Z"},{"alias_kind":"arxiv_version","alias_value":"2506.09282v1","created_at":"2026-07-05T11:19:34Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.09282","created_at":"2026-07-05T11:19:34Z"},{"alias_kind":"pith_short_12","alias_value":"U2JW2GOE4LRR","created_at":"2026-07-05T11:19:34Z"},{"alias_kind":"pith_short_16","alias_value":"U2JW2GOE4LRRHY5Q","created_at":"2026-07-05T11:19:34Z"},{"alias_kind":"pith_short_8","alias_value":"U2JW2GOE","created_at":"2026-07-05T11:19:34Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:U2JW2GOE4LRRHY5QJJVKOC5BCW","target":"record","payload":{"canonical_record":{"source":{"id":"2506.09282","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.DC","submitted_at":"2025-06-10T22:46:12Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"361f44777d92608fee3540a13875f092aebc5d6c7e2daeebc13600945ae65bd1","abstract_canon_sha256":"d0101c991f921a8dbd8054d58220dbf27046073d5921be0c1c5fc6ce63820627"},"schema_version":"1.0"},"canonical_sha256":"a6936d19c4e2e313e3b04a6aa70ba115932030bd1d10ae9c6ccc7e55600957ab","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:19:34.111770Z","signature_b64":"SXmFiUmZe8w9sLxhwlMHxQL1YKw5RwHUNxo54A11jW0uvPc2MLOuTfKmZD3kWhNoCJ57jmcbDz7ic5DMTRrZCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a6936d19c4e2e313e3b04a6aa70ba115932030bd1d10ae9c6ccc7e55600957ab","last_reissued_at":"2026-07-05T11:19:34.111233Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:19:34.111233Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2506.09282","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":"2026-07-05T11:19:34Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"g4HFa09sMNZofnn0V+g6dQii+t+H+XiLIuGW0bKynP2tEOW41DUUJuSeQl30djrrS2zbbPKjmCKe/r1PHxh5AQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-15T21:39:48.823922Z"},"content_sha256":"05c999cafa3af31f7b4cd2a114c15321aabd68507e330975ef2d0fba8118e63d","schema_version":"1.0","event_id":"sha256:05c999cafa3af31f7b4cd2a114c15321aabd68507e330975ef2d0fba8118e63d"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:U2JW2GOE4LRRHY5QJJVKOC5BCW","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"ScalableHD: Scalable and High-Throughput Hyperdimensional Computing Inference on Multi-Core CPUs","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.DC","authors_text":"Dhruv Parikh, Viktor Prasanna","submitted_at":"2025-06-10T22:46:12Z","abstract_excerpt":"Hyperdimensional Computing (HDC) is a brain-inspired computing paradigm that represents and manipulates information using high-dimensional vectors, called hypervectors (HV). Traditional HDC methods, while robust to noise and inherently parallel, rely on single-pass, non-parametric training and often suffer from low accuracy. To address this, recent approaches adopt iterative training of base and class HVs, typically accelerated on GPUs. Inference, however, remains lightweight and well-suited for real-time execution. Yet, efficient HDC inference has been studied almost exclusively on specialize"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.09282","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2506.09282/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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":"2026-07-05T11:19:34Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"sHB4aEIN42gaDZRDqVjIGggYqOSrzixrIFzfO7epxKUQirCv2/LHUgempMCizYJlAoW3YEl1BBUyoh6GvuNOAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-15T21:39:48.824599Z"},"content_sha256":"9e6bdf8ef14fe501e1f4bec8228b33c7b18cc817a05207bf247450ba781957f0","schema_version":"1.0","event_id":"sha256:9e6bdf8ef14fe501e1f4bec8228b33c7b18cc817a05207bf247450ba781957f0"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/U2JW2GOE4LRRHY5QJJVKOC5BCW/bundle.json","state_url":"https://pith.science/pith/U2JW2GOE4LRRHY5QJJVKOC5BCW/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/U2JW2GOE4LRRHY5QJJVKOC5BCW/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":"2026-08-15T21:39:48Z","links":{"resolver":"https://pith.science/pith/U2JW2GOE4LRRHY5QJJVKOC5BCW","bundle":"https://pith.science/pith/U2JW2GOE4LRRHY5QJJVKOC5BCW/bundle.json","state":"https://pith.science/pith/U2JW2GOE4LRRHY5QJJVKOC5BCW/state.json","well_known_bundle":"https://pith.science/.well-known/pith/U2JW2GOE4LRRHY5QJJVKOC5BCW/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:U2JW2GOE4LRRHY5QJJVKOC5BCW","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":"d0101c991f921a8dbd8054d58220dbf27046073d5921be0c1c5fc6ce63820627","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.DC","submitted_at":"2025-06-10T22:46:12Z","title_canon_sha256":"361f44777d92608fee3540a13875f092aebc5d6c7e2daeebc13600945ae65bd1"},"schema_version":"1.0","source":{"id":"2506.09282","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2506.09282","created_at":"2026-07-05T11:19:34Z"},{"alias_kind":"arxiv_version","alias_value":"2506.09282v1","created_at":"2026-07-05T11:19:34Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.09282","created_at":"2026-07-05T11:19:34Z"},{"alias_kind":"pith_short_12","alias_value":"U2JW2GOE4LRR","created_at":"2026-07-05T11:19:34Z"},{"alias_kind":"pith_short_16","alias_value":"U2JW2GOE4LRRHY5Q","created_at":"2026-07-05T11:19:34Z"},{"alias_kind":"pith_short_8","alias_value":"U2JW2GOE","created_at":"2026-07-05T11:19:34Z"}],"graph_snapshots":[{"event_id":"sha256:9e6bdf8ef14fe501e1f4bec8228b33c7b18cc817a05207bf247450ba781957f0","target":"graph","created_at":"2026-07-05T11:19:34Z","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/2506.09282/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Hyperdimensional Computing (HDC) is a brain-inspired computing paradigm that represents and manipulates information using high-dimensional vectors, called hypervectors (HV). Traditional HDC methods, while robust to noise and inherently parallel, rely on single-pass, non-parametric training and often suffer from low accuracy. To address this, recent approaches adopt iterative training of base and class HVs, typically accelerated on GPUs. Inference, however, remains lightweight and well-suited for real-time execution. Yet, efficient HDC inference has been studied almost exclusively on specialize","authors_text":"Dhruv Parikh, Viktor Prasanna","cross_cats":["cs.LG"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.DC","submitted_at":"2025-06-10T22:46:12Z","title":"ScalableHD: Scalable and High-Throughput Hyperdimensional Computing Inference on Multi-Core CPUs"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.09282","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":"sha256:05c999cafa3af31f7b4cd2a114c15321aabd68507e330975ef2d0fba8118e63d","target":"record","created_at":"2026-07-05T11:19:34Z","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":"d0101c991f921a8dbd8054d58220dbf27046073d5921be0c1c5fc6ce63820627","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.DC","submitted_at":"2025-06-10T22:46:12Z","title_canon_sha256":"361f44777d92608fee3540a13875f092aebc5d6c7e2daeebc13600945ae65bd1"},"schema_version":"1.0","source":{"id":"2506.09282","kind":"arxiv","version":1}},"canonical_sha256":"a6936d19c4e2e313e3b04a6aa70ba115932030bd1d10ae9c6ccc7e55600957ab","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"a6936d19c4e2e313e3b04a6aa70ba115932030bd1d10ae9c6ccc7e55600957ab","first_computed_at":"2026-07-05T11:19:34.111233Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:19:34.111233Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"SXmFiUmZe8w9sLxhwlMHxQL1YKw5RwHUNxo54A11jW0uvPc2MLOuTfKmZD3kWhNoCJ57jmcbDz7ic5DMTRrZCA==","signature_status":"signed_v1","signed_at":"2026-07-05T11:19:34.111770Z","signed_message":"canonical_sha256_bytes"},"source_id":"2506.09282","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:05c999cafa3af31f7b4cd2a114c15321aabd68507e330975ef2d0fba8118e63d","sha256:9e6bdf8ef14fe501e1f4bec8228b33c7b18cc817a05207bf247450ba781957f0"],"state_sha256":"6770b05c7b373cd6108960c83df8556bd25ca5b4f2e303f8620c52a5063ea97f"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"o75Cgtgx9bUphbwlwxwTnY+FyfDSSqDKvGIQnug1Mz9f7L0FlkZouMqwotMrZ1V4sMv4Fup7wQq0dBv9BHuaCw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-15T21:39:48.829414Z","bundle_sha256":"0b4489dfe1e3da2fc8250462eb0c6d933044e5d2b007d0a677fb038221abe36d"}}