2 papers
cs.AR2026
Bit-Width-Aware Design Environment for Few-Shot Learning on Edge AI Hardware
R. Kanda, H. L. Blevec, N. Onizawa +3
In this study, we propose an implementation methodology of real-time few-shot learning on tiny FPGA SoCs such as the PYNQ-Z1 board with arbitrary fixed-point bit-widths. Tensil-bas…
cs.AR2026
Design Environment of Quantization-Aware Edge AI Hardware for Few-Shot Learning
R. Kanda, N. Onizawa, M. Leonardon +2
This study aims to ensure consistency in accuracy throughout the entire design flow in the implementation of edge AI hardware for few-shot learning, by implementing fixed-point dat…