{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2016:2DEE6DEMJV42ZNU7MGXNBZHA7L","short_pith_number":"pith:2DEE6DEM","schema_version":"1.0","canonical_sha256":"d0c84f0c8c4d79acb69f61aed0e4e0fad418b9b0eb3ee91b88587f146130ed90","source":{"kind":"arxiv","id":"1608.03175","version":2},"attestation_state":"computed","paper":{"title":"Similarity Search on Automata Processors","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.DC","authors_text":"Armin Alaghi, Carlo C. Del Mundo, Justin Kotalik, Luis Ceze, Mark Oskin, Vincent T. Lee","submitted_at":"2016-08-09T17:27:12Z","abstract_excerpt":"Similarity search is a critical primitive for a wide variety of applications including natural language processing, content-based search, machine learning, computer vision, databases, robotics, and recommendation systems. At its core, similarity search is implemented using the k-nearest neighbors (kNN) algorithm, where computation consists of highly parallel distance calculations and a global top-k sort. In contemporary von-Neumann architectures, kNN is bottlenecked by data movement which limits throughput and latency. In this paper, we present and evaluate a novel automata-based algorithm for"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"1608.03175","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.DC","submitted_at":"2016-08-09T17:27:12Z","cross_cats_sorted":[],"title_canon_sha256":"3a17b39e231e51f0ed3084fd3a0e5e9d3f0f71a32d77992671f5138b14e19f67","abstract_canon_sha256":"b97cd22e170133b542669a441e7180b572394ea65ccc326191ca84661c4f1832"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:42:46.529735Z","signature_b64":"Awe3Vsi/f7st1YWVSkTAL0PrfBoweGeZKHtrtU0opsyMQu7YCGM5cCjXvMWjNOuNkzvfYqBRJQvv8Rs8UuJMDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d0c84f0c8c4d79acb69f61aed0e4e0fad418b9b0eb3ee91b88587f146130ed90","last_reissued_at":"2026-05-18T00:42:46.529092Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:42:46.529092Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Similarity Search on Automata Processors","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.DC","authors_text":"Armin Alaghi, Carlo C. Del Mundo, Justin Kotalik, Luis Ceze, Mark Oskin, Vincent T. Lee","submitted_at":"2016-08-09T17:27:12Z","abstract_excerpt":"Similarity search is a critical primitive for a wide variety of applications including natural language processing, content-based search, machine learning, computer vision, databases, robotics, and recommendation systems. At its core, similarity search is implemented using the k-nearest neighbors (kNN) algorithm, where computation consists of highly parallel distance calculations and a global top-k sort. In contemporary von-Neumann architectures, kNN is bottlenecked by data movement which limits throughput and latency. In this paper, we present and evaluate a novel automata-based algorithm for"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1608.03175","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"},"aliases":[{"alias_kind":"arxiv","alias_value":"1608.03175","created_at":"2026-05-18T00:42:46.529202+00:00"},{"alias_kind":"arxiv_version","alias_value":"1608.03175v2","created_at":"2026-05-18T00:42:46.529202+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1608.03175","created_at":"2026-05-18T00:42:46.529202+00:00"},{"alias_kind":"pith_short_12","alias_value":"2DEE6DEMJV42","created_at":"2026-05-18T12:29:55.572404+00:00"},{"alias_kind":"pith_short_16","alias_value":"2DEE6DEMJV42ZNU7","created_at":"2026-05-18T12:29:55.572404+00:00"},{"alias_kind":"pith_short_8","alias_value":"2DEE6DEM","created_at":"2026-05-18T12:29:55.572404+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/2DEE6DEMJV42ZNU7MGXNBZHA7L","json":"https://pith.science/pith/2DEE6DEMJV42ZNU7MGXNBZHA7L.json","graph_json":"https://pith.science/api/pith-number/2DEE6DEMJV42ZNU7MGXNBZHA7L/graph.json","events_json":"https://pith.science/api/pith-number/2DEE6DEMJV42ZNU7MGXNBZHA7L/events.json","paper":"https://pith.science/paper/2DEE6DEM"},"agent_actions":{"view_html":"https://pith.science/pith/2DEE6DEMJV42ZNU7MGXNBZHA7L","download_json":"https://pith.science/pith/2DEE6DEMJV42ZNU7MGXNBZHA7L.json","view_paper":"https://pith.science/paper/2DEE6DEM","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1608.03175&json=true","fetch_graph":"https://pith.science/api/pith-number/2DEE6DEMJV42ZNU7MGXNBZHA7L/graph.json","fetch_events":"https://pith.science/api/pith-number/2DEE6DEMJV42ZNU7MGXNBZHA7L/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/2DEE6DEMJV42ZNU7MGXNBZHA7L/action/timestamp_anchor","attest_storage":"https://pith.science/pith/2DEE6DEMJV42ZNU7MGXNBZHA7L/action/storage_attestation","attest_author":"https://pith.science/pith/2DEE6DEMJV42ZNU7MGXNBZHA7L/action/author_attestation","sign_citation":"https://pith.science/pith/2DEE6DEMJV42ZNU7MGXNBZHA7L/action/citation_signature","submit_replication":"https://pith.science/pith/2DEE6DEMJV42ZNU7MGXNBZHA7L/action/replication_record"}},"created_at":"2026-05-18T00:42:46.529202+00:00","updated_at":"2026-05-18T00:42:46.529202+00:00"}