Radar-PIM: Developing IoT Processors Utilizing Processing-in-Memory Architecture for Ultra-Wideband Radar-Based Respiration Detection

초록

The adoption of Ultra-Wideband (UWB) radar technology in IoT and healthcare applications for respiration detection is rapidly expanding, opening up a wide array of potential use cases. Despite its burgeoning utility, the integration of UWB radar-based respiration detection in IoT endnode devices faces significant challenges due to the memory-intensive nature of these tasks, which strain the capabilities of IoT processors. This paper introduces a streamlined UWB radar-based respiration detection application designed for operation on IoT processors, emphasizing that when executed on conventional IoT processors, the limited processing power still results in significant data loss, underscoring the need for enhanced processing solutions. To address these challenges, we propose the adoption of Processing-in-Memory (PIM) technology and unveil the novel Radar-PIM architecture. This architecture is meticulously engineered to boost the efficiency of respiration detection while ensuring seamless integration with existing embedded processor frameworks. The paper extensively describes the Radar-PIM architecture and its operational mechanisms. We further demonstrate its superior performance by implementing and empirically testing a Radar-PIM processor prototype. Next, we present an optimization strategy tailored for designing energy-efficient Radar-PIM processors, specifically adapted for diverse UWB radar-based respiration detection applications. For instance, a Radar-PIM processor prototype, optimized for a particular application, achieved approximately 42% energy savings compared to its unoptimized counterpart and delivered performance nearly three times greater than that of a multicore processor with equivalent power consumption. This demonstrates the transformative potential of our proposed solution in enhancing the capabilities of radar-based respiration detection systems for IoT endnodes.

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