Neuromorphic Spiking Neural Networks for Low-Latency Autonomous Navigation

  • Authors

    • Dr. Arvind Kumar Singh Professor, Jawaharlal Nehru University, India Author
    • Dr. Lakshmi Narayanan Associate Professor, Bharathidasan University, India Author

    DOI:

    https://doi.org/10.67228/30715725/IJIARE-2018PII8F1W

    Published 08-03-2018

  • Neuromorphic Computing, Spiking Neural Networks, Autonomous Navigation, Low-Latency Systems, Event-Driven Processing, Robotics, Energy-Efficient AI, Real-Time Decision Making

    Issue

    Vol. 1 No. 2 (2018)

    Section

    Articles

    How to Cite

    [1]
    A. K. Singh and L. Narayanan, “Neuromorphic Spiking Neural Networks for Low-Latency Autonomous Navigation”, IJIARE, vol. 1, no. 2, pp. 01–11, Aug. 2018, doi: 10.67228/30715725/IJIARE-2018PII8F1W.

  • 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.

  • References

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    [11] Schuman, C. D. et al., “A survey of neuromorphic computing and neural networks in hardware,” arXiv, 2017.

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