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Communication Dans Un Congrès Année : 2024

PEFSL: A deployment Pipeline for Embedded Few-Shot Learning on a FPGA SoC

Résumé

This paper tackles the challenges of implementing few-shot learning on embedded systems, specifically FPGA SoCs, a vital approach for adapting to diverse classification tasks, especially when the costs of data acquisition or labeling prove to be prohibitively high. Our contributions encompass the development of an end-to-end open-source pipeline for a few-shot learning platform for object classification on a FPGA SoCs. The pipeline is built on top of the Tensil open-source framework, facilitating the design, training, evaluation, and deployment of DNN backbones tailored for few-shot learning. Additionally, we showcase our work’s potential by building and deploying a low-power, low-latency demonstrator trained on the MiniImageNet dataset with a dataflow architecture. The proposed system has a latency of 30 ms while consuming 6.2 W on the PYNQ-Z1 board.
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Dates et versions

hal-04559365 , version 1 (26-04-2024)

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  • HAL Id : hal-04559365 , version 1

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Lucas Grativol Ribeiro, Lubin Gauthier, Mathieu Leonardon, Jérémy Morlier, Antoine Lavrard-Meyer, et al.. PEFSL: A deployment Pipeline for Embedded Few-Shot Learning on a FPGA SoC. ISCAS 2024 : IEEE International Symposium on Circuits and Systems, May 2024, Singapore, Singapore. ⟨hal-04559365⟩
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