InstantFT fine-tunes a LeNet-5-like CNN on a Xilinx Kria KV260 FPGA in 0.36 seconds, with accuracy close to LoRA-All, by caching frozen features and training low-rank adapters that connect every layer to the output.
A Neural Network-Based On-device Learning Anomaly Detector for Edge Devices.IEEE Transactions on Computers, 69(7):1027–1044, July 2020
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
citation-role summary
background 1
citation-polarity summary
fields
cs.LG 1years
2025 1verdicts
CONDITIONAL 1roles
background 1polarities
unclear 1representative citing papers
citing papers explorer
-
InstantFT: An FPGA-Based Runtime Subsecond Fine-tuning of CNN Models
InstantFT fine-tunes a LeNet-5-like CNN on a Xilinx Kria KV260 FPGA in 0.36 seconds, with accuracy close to LoRA-All, by caching frozen features and training low-rank adapters that connect every layer to the output.