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.
Addressing Gap between Training Data and Deployed Environment by On-Device Learning.IEEE Micro, 43(6):66–73, Nov/Dec 2023
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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.