{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:WTGZJ5J3ZT5GGVKLWOOQVWGC2E","short_pith_number":"pith:WTGZJ5J3","schema_version":"1.0","canonical_sha256":"b4cd94f53bccfa63554bb39d0ad8c2d111c632fb093c74a795ddbf8376de7e25","source":{"kind":"arxiv","id":"1904.04421","version":1},"attestation_state":"computed","paper":{"title":"FPGA/DNN Co-Design: An Efficient Design Methodology for IoT Intelligence on the Edge","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Cong Hao, Deming Chen, Jinjun Xiong, Kyle Rupnow, Sitao Huang, Wen-Mei Hwu, Xiaofan Zhang, Yuhong Li","submitted_at":"2019-04-09T02:06:16Z","abstract_excerpt":"While embedded FPGAs are attractive platforms for DNN acceleration on edge-devices due to their low latency and high energy efficiency, the scarcity of resources of edge-scale FPGA devices also makes it challenging for DNN deployment. In this paper, we propose a simultaneous FPGA/DNN co-design methodology with both bottom-up and top-down approaches: a bottom-up hardware-oriented DNN model search for high accuracy, and a top-down FPGA accelerator design considering DNN-specific characteristics. We also build an automatic co-design flow, including an Auto-DNN engine to perform hardware-oriented "},"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":"1904.04421","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-04-09T02:06:16Z","cross_cats_sorted":[],"title_canon_sha256":"1547c5e05876a19039821906bb021a028100f015b77b3b47ba1467fae6edb3b8","abstract_canon_sha256":"9772aef7e83324036e45975128e8007e6c976c2887c123f8ede7dac4290cac87"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:48:59.043702Z","signature_b64":"/+nhDWXdxtTzLBXk2Kk4z7qUDAKTbrBZpO69g10ioHEKE/bnh2r9lYIcETLHGMTHnZLgkU0VZ7bTLDiQERBzAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b4cd94f53bccfa63554bb39d0ad8c2d111c632fb093c74a795ddbf8376de7e25","last_reissued_at":"2026-05-17T23:48:59.043272Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:48:59.043272Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"FPGA/DNN Co-Design: An Efficient Design Methodology for IoT Intelligence on the Edge","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Cong Hao, Deming Chen, Jinjun Xiong, Kyle Rupnow, Sitao Huang, Wen-Mei Hwu, Xiaofan Zhang, Yuhong Li","submitted_at":"2019-04-09T02:06:16Z","abstract_excerpt":"While embedded FPGAs are attractive platforms for DNN acceleration on edge-devices due to their low latency and high energy efficiency, the scarcity of resources of edge-scale FPGA devices also makes it challenging for DNN deployment. In this paper, we propose a simultaneous FPGA/DNN co-design methodology with both bottom-up and top-down approaches: a bottom-up hardware-oriented DNN model search for high accuracy, and a top-down FPGA accelerator design considering DNN-specific characteristics. We also build an automatic co-design flow, including an Auto-DNN engine to perform hardware-oriented "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1904.04421","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":""},"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":"1904.04421","created_at":"2026-05-17T23:48:59.043361+00:00"},{"alias_kind":"arxiv_version","alias_value":"1904.04421v1","created_at":"2026-05-17T23:48:59.043361+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1904.04421","created_at":"2026-05-17T23:48:59.043361+00:00"},{"alias_kind":"pith_short_12","alias_value":"WTGZJ5J3ZT5G","created_at":"2026-05-18T12:33:30.264802+00:00"},{"alias_kind":"pith_short_16","alias_value":"WTGZJ5J3ZT5GGVKL","created_at":"2026-05-18T12:33:30.264802+00:00"},{"alias_kind":"pith_short_8","alias_value":"WTGZJ5J3","created_at":"2026-05-18T12:33:30.264802+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/WTGZJ5J3ZT5GGVKLWOOQVWGC2E","json":"https://pith.science/pith/WTGZJ5J3ZT5GGVKLWOOQVWGC2E.json","graph_json":"https://pith.science/api/pith-number/WTGZJ5J3ZT5GGVKLWOOQVWGC2E/graph.json","events_json":"https://pith.science/api/pith-number/WTGZJ5J3ZT5GGVKLWOOQVWGC2E/events.json","paper":"https://pith.science/paper/WTGZJ5J3"},"agent_actions":{"view_html":"https://pith.science/pith/WTGZJ5J3ZT5GGVKLWOOQVWGC2E","download_json":"https://pith.science/pith/WTGZJ5J3ZT5GGVKLWOOQVWGC2E.json","view_paper":"https://pith.science/paper/WTGZJ5J3","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1904.04421&json=true","fetch_graph":"https://pith.science/api/pith-number/WTGZJ5J3ZT5GGVKLWOOQVWGC2E/graph.json","fetch_events":"https://pith.science/api/pith-number/WTGZJ5J3ZT5GGVKLWOOQVWGC2E/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/WTGZJ5J3ZT5GGVKLWOOQVWGC2E/action/timestamp_anchor","attest_storage":"https://pith.science/pith/WTGZJ5J3ZT5GGVKLWOOQVWGC2E/action/storage_attestation","attest_author":"https://pith.science/pith/WTGZJ5J3ZT5GGVKLWOOQVWGC2E/action/author_attestation","sign_citation":"https://pith.science/pith/WTGZJ5J3ZT5GGVKLWOOQVWGC2E/action/citation_signature","submit_replication":"https://pith.science/pith/WTGZJ5J3ZT5GGVKLWOOQVWGC2E/action/replication_record"}},"created_at":"2026-05-17T23:48:59.043361+00:00","updated_at":"2026-05-17T23:48:59.043361+00:00"}