Processing-in-Memory (PIM) architectures offer promising solutions for efficiently handling AI applications in energy-constrained edge environments. While traditional PIM designs enhance performance and energy efficiency by reducing data movement between memory and processing units, they are limited in edge devices due to continuous power demands and the storage requirements of large neural network weights in SRAM and DRAM. Hybrid PIM architectures, incorporating nonvolatile memories like MRAM and ReRAM, mitigate these limitations but struggle with a mismatch between fixed computing resources and dynamically changing inference workloads. To address these challenges, this study introduces a Heterogeneous-Hybrid PIM (HH-PIM) architecture, comprising high-performance MRAM-SRAM PIM modules and low-power MRAM-SRAM PIM modules. We further propose a data placement optimization algorithm that dynamically allocates data based on computational demand, maximizing energy efficiency. FPGA prototyping and power simulations with processors featuring HH-PIM and other PIM types demonstrate that the proposed HH-PIM achieves up to 60.43% average energy savings over conventional PIMs while meeting application latency requirements. These results confirm HH-PIM’s suitability for adaptive, energy-efficient AI processing in edge devices.

We recently proposed Radar-PIM, a Processing-in-Memory (PIM) solution for real-time, low-power UWB radar respiration detection. To meet stringent energy constraints for UAV-based rescue operations, we further developed Radar-PIM-Lite, significantly reducing resource usage and power consumption. We implemented a processor based on our proposed technology and validated its superior ultra-low-power performance and reliable real-time detection capability through FPGA prototyping and application demonstrations. We will showcase this FPGA-based Radar-PIM-Lite prototype through a live demonstration at ISLPED 2025.

The adoption of ultrawideband (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 end node devices faces significant challenges due to the memory-intensive nature of these tasks, which strain the capabilities of IoT processors. This article 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. This article 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 end nodes.