Over the past decade, wearable medical devices (WMDs) have become the norm for continuous health monitoring, enabling real-time vital sign analysis and preventive healthcare. These battery-powered devices face computational power, size, and energy resource constraints. Traditionally, low-power microcontrollers (MCUs) and application-specific integrated circuits (ASICs) have been used for their energy efficiency. However, the increasing demand for multi-modal sensors and artificial intelligence (AI) requires more computational power than MCUs, and rapidly evolving AI asks for more flexibility, which ASICs lack. Field-programmable gate arrays (FPGAs), which are more efficient than MCUs and more flexible than ASICs, offer a potential solution when optimized for energy consumption. By combining real-time reconfigurability with intelligent energy optimization strategies, FPGAs can provide energy-efficient solutions for handling multimodal sensors and evolving AI requirements. This paper reviews low-power strategies toward FPGA-based WMD for physiological monitoring. It examines low-power FPGA families, highlighting their potential in power-sensitive applications. Future research directions are suggested, including exploring underutilized optimizations like sleep mode, voltage scaling, partial reconfiguration, and compressed learning and investigating underexplored flash and hybrid-based FPGAs. Overall, it provides guidelines for designing energy-efficient FPGA-based WMDs.
Khan, MI & da Silva, B 2024, 'Harnessing FPGA Technology for Energy-Efficient Wearable Medical Devices', Electronics, vol. 13, no. 20, 4094, pp. 1-35. https://doi.org/10.3390/electronics13204094
Khan, M. I., & da Silva, B. (2024). Harnessing FPGA Technology for Energy-Efficient Wearable Medical Devices. Electronics, 13(20), 1-35. Article 4094. https://doi.org/10.3390/electronics13204094
@article{e328d6db2b3d457296ec35e2922b5e68,
title = "Harnessing FPGA Technology for Energy-Efficient Wearable Medical Devices",
abstract = "Over the past decade, wearable medical devices (WMDs) have become the norm for continuous health monitoring, enabling real-time vital sign analysis and preventive healthcare. These battery-powered devices face computational power, size, and energy resource constraints. Traditionally, low-power microcontrollers (MCUs) and application-specific integrated circuits (ASICs) have been used for their energy efficiency. However, the increasing demand for multi-modal sensors and artificial intelligence (AI) requires more computational power than MCUs, and rapidly evolving AI asks for more flexibility, which ASICs lack. Field-programmable gate arrays (FPGAs), which are more efficient than MCUs and more flexible than ASICs, offer a potential solution when optimized for energy consumption. By combining real-time reconfigurability with intelligent energy optimization strategies, FPGAs can provide energy-efficient solutions for handling multimodal sensors and evolving AI requirements. This paper reviews low-power strategies toward FPGA-based WMD for physiological monitoring. It examines low-power FPGA families, highlighting their potential in power-sensitive applications. Future research directions are suggested, including exploring underutilized optimizations like sleep mode, voltage scaling, partial reconfiguration, and compressed learning and investigating underexplored flash and hybrid-based FPGAs. Overall, it provides guidelines for designing energy-efficient FPGA-based WMDs.",
keywords = "power optimization, energy efficiency, wearable medical devices, continuous physiological monitoring, healthcare",
author = "Khan, \{Muhammad Iqbal\} and \{da Silva\}, Bruno",
note = "Publisher Copyright: {\textcopyright} 2024 by the authors.",
year = "2024",
month = oct,
day = "17",
doi = "10.3390/electronics13204094",
language = "English",
volume = "13",
pages = "1--35",
journal = "Electronics",
issn = "2079-9292",
publisher = "MDPI AG",
number = "20",
}