{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:NBTYT5KMEUZ4K4FHKU5UNMMLRL","short_pith_number":"pith:NBTYT5KM","schema_version":"1.0","canonical_sha256":"686789f54c2533c570a7553b46b18b8af51556ba03bc51907faa42c71fe48d4c","source":{"kind":"arxiv","id":"2506.06505","version":1},"attestation_state":"computed","paper":{"title":"InstantFT: An FPGA-Based Runtime Subsecond Fine-tuning of CNN Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AR"],"primary_cat":"cs.LG","authors_text":"Hiroki Matsutani, Keisuke Sugiura","submitted_at":"2025-06-06T20:01:09Z","abstract_excerpt":"Training deep neural networks (DNNs) requires significantly more computation and memory than inference, making runtime adaptation of DNNs challenging on resource-limited IoT platforms. We propose InstantFT, an FPGA-based method for ultra-fast CNN fine-tuning on IoT devices, by optimizing the forward and backward computations in parameter-efficient fine-tuning (PEFT). Experiments on datasets with concept drift demonstrate that InstantFT fine-tunes a pre-trained CNN 17.4x faster than existing Low-Rank Adaptation (LoRA)-based approaches, while achieving comparable accuracy. Our FPGA-based Instant"},"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":"2506.06505","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-06-06T20:01:09Z","cross_cats_sorted":["cs.AR"],"title_canon_sha256":"e30b55ca4cbbd64183ab8eed38cdb8d69b2f40c3b6d7b0e8d5e98754a7fc6692","abstract_canon_sha256":"dc0389f8e56fcabc27a1fa1f14fb5aa521948e74a14a359afe48b08546f65e6f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:17:51.310475Z","signature_b64":"iJl7scDWhHgRWah5Qq0Jqdw8mGN6rvQTHnz/dLju3TWJIxNCGSo9T5HuExS9kYzqeq00oMV99qTFKYFUsTzSDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"686789f54c2533c570a7553b46b18b8af51556ba03bc51907faa42c71fe48d4c","last_reissued_at":"2026-07-05T11:17:51.310031Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:17:51.310031Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"InstantFT: An FPGA-Based Runtime Subsecond Fine-tuning of CNN Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AR"],"primary_cat":"cs.LG","authors_text":"Hiroki Matsutani, Keisuke Sugiura","submitted_at":"2025-06-06T20:01:09Z","abstract_excerpt":"Training deep neural networks (DNNs) requires significantly more computation and memory than inference, making runtime adaptation of DNNs challenging on resource-limited IoT platforms. We propose InstantFT, an FPGA-based method for ultra-fast CNN fine-tuning on IoT devices, by optimizing the forward and backward computations in parameter-efficient fine-tuning (PEFT). Experiments on datasets with concept drift demonstrate that InstantFT fine-tunes a pre-trained CNN 17.4x faster than existing Low-Rank Adaptation (LoRA)-based approaches, while achieving comparable accuracy. Our FPGA-based Instant"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.06505","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/2506.06505/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":"2506.06505","created_at":"2026-07-05T11:17:51.310086+00:00"},{"alias_kind":"arxiv_version","alias_value":"2506.06505v1","created_at":"2026-07-05T11:17:51.310086+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.06505","created_at":"2026-07-05T11:17:51.310086+00:00"},{"alias_kind":"pith_short_12","alias_value":"NBTYT5KMEUZ4","created_at":"2026-07-05T11:17:51.310086+00:00"},{"alias_kind":"pith_short_16","alias_value":"NBTYT5KMEUZ4K4FH","created_at":"2026-07-05T11:17:51.310086+00:00"},{"alias_kind":"pith_short_8","alias_value":"NBTYT5KM","created_at":"2026-07-05T11:17:51.310086+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/NBTYT5KMEUZ4K4FHKU5UNMMLRL","json":"https://pith.science/pith/NBTYT5KMEUZ4K4FHKU5UNMMLRL.json","graph_json":"https://pith.science/api/pith-number/NBTYT5KMEUZ4K4FHKU5UNMMLRL/graph.json","events_json":"https://pith.science/api/pith-number/NBTYT5KMEUZ4K4FHKU5UNMMLRL/events.json","paper":"https://pith.science/paper/NBTYT5KM"},"agent_actions":{"view_html":"https://pith.science/pith/NBTYT5KMEUZ4K4FHKU5UNMMLRL","download_json":"https://pith.science/pith/NBTYT5KMEUZ4K4FHKU5UNMMLRL.json","view_paper":"https://pith.science/paper/NBTYT5KM","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2506.06505&json=true","fetch_graph":"https://pith.science/api/pith-number/NBTYT5KMEUZ4K4FHKU5UNMMLRL/graph.json","fetch_events":"https://pith.science/api/pith-number/NBTYT5KMEUZ4K4FHKU5UNMMLRL/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/NBTYT5KMEUZ4K4FHKU5UNMMLRL/action/timestamp_anchor","attest_storage":"https://pith.science/pith/NBTYT5KMEUZ4K4FHKU5UNMMLRL/action/storage_attestation","attest_author":"https://pith.science/pith/NBTYT5KMEUZ4K4FHKU5UNMMLRL/action/author_attestation","sign_citation":"https://pith.science/pith/NBTYT5KMEUZ4K4FHKU5UNMMLRL/action/citation_signature","submit_replication":"https://pith.science/pith/NBTYT5KMEUZ4K4FHKU5UNMMLRL/action/replication_record"}},"created_at":"2026-07-05T11:17:51.310086+00:00","updated_at":"2026-07-05T11:17:51.310086+00:00"}