Computationally Efficient Pruning Before Training Scheme for SNN‐Based Radar Gesture Recognition

ABSTRACT Spiking neural networks (SNNs)‐based radar gesture recognition has gained significant attention for its ability to enhance computational efficiency. To better exploit their potential, SNNs are often integrated with effective compression techniques to enable practical deployment in resource‐constrained edge environments. However, conventional pruning approaches either necessitate computationally intensive pre‐training or rely on gradient‐dependent mechanisms, which are incompatible with