{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:7K3D3RC2SHW32PKR7B3LIPUZNY","short_pith_number":"pith:7K3D3RC2","schema_version":"1.0","canonical_sha256":"fab63dc45a91edbd3d51f876b43e996e1b6309b49e80abaa5dea4c7aa9b2e37e","source":{"kind":"arxiv","id":"2211.02281","version":1},"attestation_state":"computed","paper":{"title":"An Efficient FPGA-based Accelerator for Deep Forest","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Jiapeng Luo, Mingyu Zhu, Wendong Mao, Zhongfeng Wang","submitted_at":"2022-11-04T06:41:46Z","abstract_excerpt":"Deep Forest is a prominent machine learning algorithm known for its high accuracy in forecasting. Compared with deep neural networks, Deep Forest has almost no multiplication operations and has better performance on small datasets. However, due to the deep structure and large forest quantity, it suffers from large amounts of calculation and memory consumption. In this paper, an efficient hardware accelerator is proposed for deep forest models, which is also the first work to implement Deep Forest on FPGA. Firstly, a delicate node computing unit (NCU) is designed to improve inference speed. Sec"},"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":"2211.02281","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2022-11-04T06:41:46Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"2d0faf47808e78088d0e31d195758a6cb3468bfcc730c5ba3b5d2ddef975039a","abstract_canon_sha256":"be592ba87968f5ec4897843d16e25398f3f28cdaf3a439c3ec31bf76a66129d6"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:13:13.594613Z","signature_b64":"RZmQVvePBxAgH5yiEOdN2QJ3fRmLCInCULqKHMr55K0o4W4MOInMTXOTrmtJe3eYqF7RDOmg6TUDlKtt4eivAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"fab63dc45a91edbd3d51f876b43e996e1b6309b49e80abaa5dea4c7aa9b2e37e","last_reissued_at":"2026-07-05T05:13:13.594264Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:13:13.594264Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"An Efficient FPGA-based Accelerator for Deep Forest","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Jiapeng Luo, Mingyu Zhu, Wendong Mao, Zhongfeng Wang","submitted_at":"2022-11-04T06:41:46Z","abstract_excerpt":"Deep Forest is a prominent machine learning algorithm known for its high accuracy in forecasting. Compared with deep neural networks, Deep Forest has almost no multiplication operations and has better performance on small datasets. However, due to the deep structure and large forest quantity, it suffers from large amounts of calculation and memory consumption. In this paper, an efficient hardware accelerator is proposed for deep forest models, which is also the first work to implement Deep Forest on FPGA. Firstly, a delicate node computing unit (NCU) is designed to improve inference speed. Sec"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2211.02281","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/2211.02281/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2211.02281","created_at":"2026-07-05T05:13:13.594333+00:00"},{"alias_kind":"arxiv_version","alias_value":"2211.02281v1","created_at":"2026-07-05T05:13:13.594333+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2211.02281","created_at":"2026-07-05T05:13:13.594333+00:00"},{"alias_kind":"pith_short_12","alias_value":"7K3D3RC2SHW3","created_at":"2026-07-05T05:13:13.594333+00:00"},{"alias_kind":"pith_short_16","alias_value":"7K3D3RC2SHW32PKR","created_at":"2026-07-05T05:13:13.594333+00:00"},{"alias_kind":"pith_short_8","alias_value":"7K3D3RC2","created_at":"2026-07-05T05:13:13.594333+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/7K3D3RC2SHW32PKR7B3LIPUZNY","json":"https://pith.science/pith/7K3D3RC2SHW32PKR7B3LIPUZNY.json","graph_json":"https://pith.science/api/pith-number/7K3D3RC2SHW32PKR7B3LIPUZNY/graph.json","events_json":"https://pith.science/api/pith-number/7K3D3RC2SHW32PKR7B3LIPUZNY/events.json","paper":"https://pith.science/paper/7K3D3RC2"},"agent_actions":{"view_html":"https://pith.science/pith/7K3D3RC2SHW32PKR7B3LIPUZNY","download_json":"https://pith.science/pith/7K3D3RC2SHW32PKR7B3LIPUZNY.json","view_paper":"https://pith.science/paper/7K3D3RC2","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2211.02281&json=true","fetch_graph":"https://pith.science/api/pith-number/7K3D3RC2SHW32PKR7B3LIPUZNY/graph.json","fetch_events":"https://pith.science/api/pith-number/7K3D3RC2SHW32PKR7B3LIPUZNY/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/7K3D3RC2SHW32PKR7B3LIPUZNY/action/timestamp_anchor","attest_storage":"https://pith.science/pith/7K3D3RC2SHW32PKR7B3LIPUZNY/action/storage_attestation","attest_author":"https://pith.science/pith/7K3D3RC2SHW32PKR7B3LIPUZNY/action/author_attestation","sign_citation":"https://pith.science/pith/7K3D3RC2SHW32PKR7B3LIPUZNY/action/citation_signature","submit_replication":"https://pith.science/pith/7K3D3RC2SHW32PKR7B3LIPUZNY/action/replication_record"}},"created_at":"2026-07-05T05:13:13.594333+00:00","updated_at":"2026-07-05T05:13:13.594333+00:00"}