计算机科学
实施
可穿戴计算机
现场可编程门阵列
人工神经网络
软件
能源消耗
嵌入式系统
硬件加速
人工智能
计算机体系结构
计算机硬件
机器学习
操作系统
生态学
生物
程序设计语言
作者
Félix Tena,Óscar Garnica,Juan Lanchares Davila,J. Ignacio Hidalgo
出处
期刊:IEEE Journal of Biomedical and Health Informatics
[Institute of Electrical and Electronics Engineers]
日期:2023-01-01
卷期号:: 1-12
被引量:1
标识
DOI:10.1109/jbhi.2023.3300511
摘要
This article proposes the first hardware implemen-tation of a low-power LSTM neural network targeting a wearable medical device designed to predict blood glucose at a 30-minute horizon. This work aims to reduce energy consumption by propos-ing new activation functions that target hardware implementation. On top of this proposal, we also prove there is room for improve-ment in energy consumption by applying neural network optimiza-tions at the algorithmic, such as quantization, and architecture level, LSTM hyperparameters, that consider the target hardware. To validate our proposal, we devise an optimized version of the neural network aimed to be wearable and, therefore, to reduce its energy consumption while preserving its accuracy as much as possible. The hardware is implemented on a Xilinx Virtex-7 FPGA VC707 Evaluation Kit. It is compared with (i) a faithful design of the original neural network implemented on the same evaluation kit, (ii) three state-of-the-art LSTM-based FPGA implementations, and (iii) software implementations running in cutting-edge smartphones:OnePlus NordTM and an Apple iPhone 13 ProTM with artificial in-telligence hardware accelerators. Our proposal consumes between ×1020 and ×7 less energy than the software implementations, being the most efficient system compared to the smartphones. On the other hand, its energy efficiency, measured in GFLOP/J, is between ×2.84 and ×7.82 greater than other state-of-the-art LSTM implementations, proving to be the most suitable implementation for a wearable system for blood glucose prediction.
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