# Harnessing FPGA Technology for Energy-Efficient Wearable Medical Devices
## 3 Related Work and Motivation
While the primary focus was on the trade-off between accuracy and resource utilization for ML algorithms, the authors also touched upon the impact of this trade-off on energy consumption in various instances. However, a detailed exploration of LPTs remained outside the scope of this paper. Similarly, the authors of [51] summarized a few power optimization strategies for FPGAs, with a primary focus on dynamic voltage and frequency scaling (DVFS), power gating, and clock gating. While the authors briefly mentioned \"algorithm-level techniques\", they did not delve into the details. Broad-level exploration of LPTs was out of their scope. Likewise, in [52], the authors reviewed various vital health parameters and their corresponding physiological signals, as well as the design considerations for smart wearable healthcare devices. It does not present LPTs. It was also observed that some of the studies concentrated on reviewing energy optimization through improvements in FPGA architecture, technology, and associated CAD tools. While some studies briefly mentioned system design level optimization, they fell short of providing a comprehensive survey with optimization of processing algorithms, communication protocols, data acquisition methods, etc. For illustration, in [53], the authors broadly examined power optimization strategies in general across various hierarchical levels of design, including architecture, circuitry, devices, and systems. While the focus of their work was technological, architectural, and CAD tool improvements, they briefly listed and discussed some optimizations at a system-design level. In contrast, our research provides a comprehensive review of LPTs at the system design level, specifically within the wearable healthcare domain. Similarly, the authors of [54] mainly reviewed power optimization techniques and strategies for FPGAs at the technology, architecture, and CAD tool levels, targeting Internet of Things (IoT) devices. However, they did not review the optimization strategies at the system design level. In contrast, some researchers explored energy and power optimization opportunities without a specific emphasis on WMDs. Therefore, such studies missed out on potential optimizations which could have taken advantage of the unique features of physiological signals for applications tailored to WMDs. For example, the authors of [55] reviewed energy optimization in edge computing from hardware architecture to the application layer in general and its broader prospects. However, application-specific optimization strategies for WMDs in physiological monitoring remained out of their main scope. Similarly, the authors of [56] surveyed techniques related to optimizing and implementing convolutional neural networks (CNNs) on FPGAs. They categorized the surveyed work to emphasize both differences and similarities. While the paper briefly mentions the impact of some acceleration techniques on energy efficiency, it lacks a detailed discussion of LPTs and their underlying principles. Additionally, the focus on WMDs as potential applications remained out of the scope of this paper. In a similar fashion, the authors in [57] examined recent research on implementing deep learning models in FPGAs. They compared different topologies and development strategies in terms of performance and energy efficiency. However, the paper did not delve into the classification of LPTs specifically related to wearable physiological monitoring; that topic was beyond the scope of their study. In another work, FPGA-related LPTs within the domain of wireless sensor networks for IoT applications were explored in detail [13]. While these LPTs can be adapted for use in WMDs, it is important to note that this paper does not delve into the specifics of such utilization. In contrast, the review presented in [58] is limited to communication-related optimizations only. There are several other review papers on energy-efficient health monitoring at the edge. However, their main focus is on allied technologies rather than the computing platform.