We introduce NeuroRadar, a novel low-power radar paradigm that realizes the concept of neuromorphic radar sensing. NeuroRadar incorporates a spike-generation radar sensor that directly interfaces with SNN-based neuromorphic processors, leading to superior energy efficiency. We devise a low-power, low-complexity radar front end based on the SIL principle. Both our theoretical analysis and experimental results demonstrate that multi-chain SIL radar sensors can supply ample information for short-range, low-velocity sensing applications. We implement the neuromorphic radar system through a printed-circuit board (PCB) prototype and carry out simulations for the IC version. Our experiments verify NeuroRadar's ability to empower resource-constrained IoT devices to perform low-power smart sensing.

Interest in miniature radar systems has grown dramatically in recent years as they enable rich interaction and health monitoring in everyday settings. By 2025, industrial radar applications are anticipated to encompass 10 million devices, whereas the consumer market will reach a substantial 250 million. The applications are diverse--for example, Google's Pixel phones incorporated radar for gesture control, while small radar sensors are being deployed in homes to monitor elderly residents' movements and detect falls, offering more privacy than camera-based solutions. However, conventional radar architectures rely on complex RF front ends with power amplifiers, low-noise amplifiers, and phase-locked loops, collectively consuming hundreds of milliwatts of power. This makes radar sensing impractical for battery-powered or self-powered Internet of Things (IoT) devices and wearables.