一个基于LSTM的神经网络可穿戴系统用于糖尿病患者的血糖预测
IEEE journal of biomedical and health informatics
|August 1, 2023
概括
本研究介绍了首个低功耗硬件实现长期短期记忆 (LSTM) 神经网络,用于可穿戴血糖预测. 优化的设计显著降低了能源消耗,同时保持了精度,超过了智能手机AI加速器的性能.
科学领域:
- 生物医学工程 生物医学工程
- 计算机科学 计算机科学
- 人工智能的人工智能
背景情况:
- 可穿戴医疗设备需要高能效的加工,以进行持续监控.
- 准确的血糖预测对于糖尿病管理至关重要.
- 现有的长期短期内存 (LSTM) 实现通常缺乏可穿戴应用所需的能源效率.
研究的目的:
- 提出并实施低功耗的LSTM神经网络硬件,用于可穿戴血糖预测.
- 通过新的激活功能和算法/架构改进来优化能源消耗.
- 通过对最先进的硬件和软件解决方案验证拟议系统的性能.
主要方法:
- 为LSTM网络开发新的硬件定向激活功能.
- 应用算法 (量子化) 和架构 (超参数调整) 的优化.
- 在Xilinx Virtex-7 FPGA上硬件实现,并与软件和其他基于FPGA的LSTM进行比较.
- 能源消耗和能源效率的评估 (GFLOP/J).
主要成果:
- 拟议的硬件实现实现的能源消耗比智能手机软件实现低10-20倍.
- 能源效率比其他最先进的LSTM FPGA实现高2.84-7.82倍.
- 优化的LSTM保持了准确性,同时显著降低了功率需求.
结论:
- 开发的低功耗LSTM硬件是可穿戴血糖预测的最节能解决方案.
- 新的激活功能和有针对性的优化是实现可穿戴人工智能的高能效率的关键.
- 这项工作为更可持续,更有效的AI医疗设备铺平了道路.
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