Energy-Efficient Frequency Selection Method for Bio-Signal Acquisition in AI/ML Wearables

In wearable sensors, energy efficiency is crucial, particularly during phases where devices are not processing, but rather acquiring biosignals for subsequent analysis. This study focuses on improving the power consumption of wearables during these acquisition phases, a critical but often overlooked aspect that substantially affects overall device energy consumption, especially in low-duty-cycle applications. Our approach optimizes power consumption by leveraging application-specific requirements (e.g., required signal profile), platform characteristics (e.g., transition-time overhead for the clock generators and power-gating capabilities), and analog biosignal front-end specifications (e.g., ADC buffer sizes). We refine the strategy for switching between low-power idle and active states for the storage of acquired data, introducing a novel method to select optimal frequencies for these states. Based on several case studies on an ultra-low power platform and different biomedical applications, our optimization methodology achieves substantial energy savings. For example, in a 12-lead heartbeat classification task, our method reduces total energy consumption by up to 58% compared to state-of-the-art methods. This research provides a theoretical basis for frequency optimization and practical insights, including characterizing the platform's power and overheads for optimization purposes. Our findings significantly improve energy efficiency during the acquisition phase of wearable devices, thus extending their operational lifespan.