Neuromorphic Spiking Neural Networks for Low-Latency Autonomous Navigation

Abstract

Low-latency decision-making is a critical requirement for autonomous navigation in dynamic and resource-constrained environments. Conventional deep learning-based navigation systems often suffer from high computational overhead and energy consumption, limiting their deployment in real-time robotic applications. This paper presents a neuromorphic navigation framework based on spiking neural networks (SNNs) that leverages event-driven computation for efficient perception and control. The proposed system integrates biologically inspired neuron models with latency-aware learning mechanisms to enable rapid sensory processing and decision-making. By exploiting temporal information encoded in spike trains, the framework achieves faster response times while maintaining robust navigation performance. Experimental evaluations demonstrate that the neuromorphic SNN-based approach significantly reduces inference latency and energy consumption compared to conventional neural network baselines, making it suitable for real-time autonomous navigation tasks. The results highlight the potential of neuromorphic computing as a scalable and energy-efficient solution for next-generation autonomous systems.