{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:I6OYMRDOERBURTUUCZFFIHCBPZ","short_pith_number":"pith:I6OYMRDO","schema_version":"1.0","canonical_sha256":"479d86446e244348ce94164a541c417e7c63a07360d86437e7f5875399ffd89b","source":{"kind":"arxiv","id":"1801.07459","version":3},"attestation_state":"computed","paper":{"title":"Stacked Filters Stationary Flow For Hardware-Oriented Acceleration Of Deep Convolutional Neural Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Nianhong Liu, Sheng Zhang, Yuechao Gao","submitted_at":"2018-01-23T09:57:10Z","abstract_excerpt":"To address memory and computation resource limitations for hardware-oriented acceleration of deep convolutional neural networks (CNNs), we present a computation flow, stacked filters stationary flow (SFS), and a corresponding data encoding format, relative indexed compressed sparse filter format (CSF), to make the best of data sparsity, and simplify data handling at execution time. And we also propose a three dimensional Single Instruction Multiple Data (3D-SIMD) processor architecture to illustrate how to accelerate deep CNNs by taking advantage of SFS flow and CSF format. Comparing with the "},"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":"1801.07459","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-01-23T09:57:10Z","cross_cats_sorted":[],"title_canon_sha256":"400a4fc75bebcc05c6bdae40eb59b10f9e7aba65a1714e6c5748297f2b64df81","abstract_canon_sha256":"cd29c2fc5e1bb71e09fa67c80a3aeed22d40070a462cf43c433911d395d566d8"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:24:22.055878Z","signature_b64":"Pe4FqI5cOtd3JU53aubu4dOUSIkoKwa016WpKz4/o6BU9hXEwVcw4opeRQb+WaBhGlHxts7Lx1rlR6Bp213ECQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"479d86446e244348ce94164a541c417e7c63a07360d86437e7f5875399ffd89b","last_reissued_at":"2026-05-18T00:24:22.055294Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:24:22.055294Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Stacked Filters Stationary Flow For Hardware-Oriented Acceleration Of Deep Convolutional Neural Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Nianhong Liu, Sheng Zhang, Yuechao Gao","submitted_at":"2018-01-23T09:57:10Z","abstract_excerpt":"To address memory and computation resource limitations for hardware-oriented acceleration of deep convolutional neural networks (CNNs), we present a computation flow, stacked filters stationary flow (SFS), and a corresponding data encoding format, relative indexed compressed sparse filter format (CSF), to make the best of data sparsity, and simplify data handling at execution time. And we also propose a three dimensional Single Instruction Multiple Data (3D-SIMD) processor architecture to illustrate how to accelerate deep CNNs by taking advantage of SFS flow and CSF format. Comparing with the "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1801.07459","kind":"arxiv","version":3},"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":"1801.07459","created_at":"2026-05-18T00:24:22.055369+00:00"},{"alias_kind":"arxiv_version","alias_value":"1801.07459v3","created_at":"2026-05-18T00:24:22.055369+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1801.07459","created_at":"2026-05-18T00:24:22.055369+00:00"},{"alias_kind":"pith_short_12","alias_value":"I6OYMRDOERBU","created_at":"2026-05-18T12:32:28.185984+00:00"},{"alias_kind":"pith_short_16","alias_value":"I6OYMRDOERBURTUU","created_at":"2026-05-18T12:32:28.185984+00:00"},{"alias_kind":"pith_short_8","alias_value":"I6OYMRDO","created_at":"2026-05-18T12:32:28.185984+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/I6OYMRDOERBURTUUCZFFIHCBPZ","json":"https://pith.science/pith/I6OYMRDOERBURTUUCZFFIHCBPZ.json","graph_json":"https://pith.science/api/pith-number/I6OYMRDOERBURTUUCZFFIHCBPZ/graph.json","events_json":"https://pith.science/api/pith-number/I6OYMRDOERBURTUUCZFFIHCBPZ/events.json","paper":"https://pith.science/paper/I6OYMRDO"},"agent_actions":{"view_html":"https://pith.science/pith/I6OYMRDOERBURTUUCZFFIHCBPZ","download_json":"https://pith.science/pith/I6OYMRDOERBURTUUCZFFIHCBPZ.json","view_paper":"https://pith.science/paper/I6OYMRDO","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1801.07459&json=true","fetch_graph":"https://pith.science/api/pith-number/I6OYMRDOERBURTUUCZFFIHCBPZ/graph.json","fetch_events":"https://pith.science/api/pith-number/I6OYMRDOERBURTUUCZFFIHCBPZ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/I6OYMRDOERBURTUUCZFFIHCBPZ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/I6OYMRDOERBURTUUCZFFIHCBPZ/action/storage_attestation","attest_author":"https://pith.science/pith/I6OYMRDOERBURTUUCZFFIHCBPZ/action/author_attestation","sign_citation":"https://pith.science/pith/I6OYMRDOERBURTUUCZFFIHCBPZ/action/citation_signature","submit_replication":"https://pith.science/pith/I6OYMRDOERBURTUUCZFFIHCBPZ/action/replication_record"}},"created_at":"2026-05-18T00:24:22.055369+00:00","updated_at":"2026-05-18T00:24:22.055369+00:00"}