Energy-efficient radar detection with spiking neural resonators via activity-gated sparsity on Intel Loihi 2

Abstract Radar sensors are a corner stone of autonomous driving, offering reliable perception under adverse weather and lighting conditions. However, the increasing resolution of modern automotive radar systems generates large data volumes that must be processed in real time, imposing significant computational and energy demands. This challenge is particularly acute in energy-constrained platforms such as electric vehicles and embedded devices, where power efficiency is critical. Neuromorphic computing offers a promising alternative by emulating the brain’s event-driven and energy-efficient information processing. In this work, we extend existing resonate-and-fire neuron models, called spiking neural resonators (SpiNRs), into the Doppler domain to enable velocity estimation. We integrate SpiNR with a spiking ordered statistics constant false alarm rate (OS-CFAR) algorithm to realize a full neuromorphic peak detection. Crucially, we introduce a novel activity-gated sparsity mechanism that dynamically deactivates inactive resonators, substantially reducing energy consumption while preserving estimation fidelity. All neuromorphic algorithms are implemented on Intel Loihi 2 neuromorphic processor, which allows us to exploit event-driven computation and benchmark against conventional digital implementations under realistic hardware constraints. Evaluation against the conventional fast Fourier transform and classical OS-CFAR pipeline demonstrates that SpinR achieves competitive accuracy in range-velocity estimation. The proposed activity-gated sparsity mechanism yields additional energy savings and removes the need for a separate peak detection stage, further simplifying the processing chain. These findings highlight the potential of neuromorphic radar processing as a power-efficient alternative to conventional methods and underscore the importance of developing next-generation neuromorphic substrates optimized for embedded signal processing.