# Harnessing FPGA Technology for Energy-Efficient Wearable Medical Devices
## 6 Analyses and Discussion
However, careful design of such optimization strategies is essential, as they can impact the accuracy and precision of results, which are critical in physiological monitoring. **Minimizing the Transmission Energy Cost**: Since wireless data transmission contributes significantly to the power consumption of WMDs [20], researchers have employed different data reduction techniques, including _data compression, compressed sensing,_ and _compressed learning_, along with _power-aware transmission strategies_. However, traditional compression methods necessitate a reconstruction step at the receiver, incurring additional computational costs and power usage. To handle this, some studies explored a _compressed learning_ approach, where data acquired through sub-Nyquist sampling are directly processed. The effectiveness of this approach relies on the sparsity of the signal. FPGAs offer the opportunity to minimize the latency introduced by compression and reconstruction processes due to their capability of parallel execution. Additionally, the integration of AI with digital signal processing for compressed learning can also benefit from this flexibility of FPGAs. Moreover, this parallelism opportunity is also well suited for the compression of multimodal signals simultaneously [109]. However, while compression techniques offer significant advantages in terms of data volume reduction and power consumption, they also introduce challenges which need to be carefully managed to ensure the accuracy and reliability of physiological monitoring in wearable medical devices. **Energy-Efficient Task Scheduling:** Tasks scheduling is another area where we observed an increased focus of researchers to reduce idle power consumption. The designers exploited the event-based nature of underlying applications to schedule tasks using either event triggers or periodic activation. Event-driven design is a reactive approach where a component is activated based on the occurrence of an event. In contrast, duty cycling is proactive, with components activated according to a preset schedule. On the other hand, the time-division multiplexing approach for hardware components leverages relaxed sampling deadlines in physiological monitoring to reduce power consumption by avoiding the simultaneous activation of multiple components. The main advantage of these scheduling approaches is that even though they introduce some latency associated with the activation time, they do not directly affect the accuracy of the results. This is highly important in physiological monitoring. Usually, the sampling deadlines in physiological monitoring are flexible [19], and thus a small delay could be acceptable. FPGAs' unique features of reconfigurability, customizability, and fine-grained control enable designers to schedule tasks more flexibly and at finer levels, optimizing both active and idle periods more effectively. These approaches significantly reduce the leakage and idle power consumption of components which are not active, along with dynamic power consumption by reducing unnecessary switching activities. However, it is crucial to consider the activation time when designing these scheduling approaches to avoid missing any critical events due to activation delays. **Exploiting FPGA Features:** On the other hand, rather few works have exploited FPGA features for power reduction, such as dynamic partial reconfiguration and custom clock gating. FPGAs allow the optimization of different design components through fine-grained power modes using custom clock gating and power gating. Specifically, when addressing the lenient sampling deadlines associated with physiological monitoring, there is an opportunity to leverage fine-grained power modes and custom clock-gating techniques. On the other hand, dynamic partial reconfiguration allows for the selective deployment of hardware components only when needed, leading to efficient resource utilization and reduced static power consumption.