2018· International Journal of Intelligent Automation & Robotics Engineering· Vol 1, pp. 01-11· 0 citations
TL;DR
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.
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.
SpikingNav achieves competitive clean performance and stronger robustness with fewer parameters and lower per-step computation than a matched ANN baseline, and physical hardware validation shows that SpikingNav can be instantiated on a real neuromorphic substrate for cyber-physical systems.
Jiahong Zhang, Sijun Shen, Dehua Wu et al.· 0 citations
It is aimed at proving that SNNs have potential in such areas as computer vision, robotics, and speech recognition, and their role in overcoming the barrier between artificial and biological neural systems is proved.
Mesala Sravani, K. Kumari, S. M. Reddy· International Journal of Unc...· 0 citations
It is demonstrated that neuromorphic-triggered optogenetic inhibition significantly alters ripple dynamics and reduces oscillatory energy, establishing a practical and accessible neuromorphic framework for low-latency closed-loop control of fast brain dynamics in vivo.
P. Félix, M. Jurado-Parras, J. Freitas et al.· bioRxiv· 0 citations
Extensive experiments on static and neuromorphic benchmarks show that lower-bit BASC models match or outperform higher-bit baselines and retain this accuracy advantage after structured pruning, while further reducing model storage and synaptic operations.
Linliang Chen, Yan Zhong, Xin Liu et al.· 0 citations
Neuromorphic engineering develops hardware and software systems inspired by biological neurons, with the goal of achieving energy-efficient, low-latency, robust, and adaptive computation, communication and control. Its potential impact on systems and control is significant, as it may enable novel approaches to control and estimation by leveraging brain-inspired computation and communication principles. In this context, we present a framework for the robust stabilization of a plant subject to disturbances when the communication between noisy sensors and the controller relies on spiking signals generated by neuron-inspired schemes. The communication scheme consists of a spike encoder on the sensors side, which is based on integrate-and-fire neurons that convert the analog plant output measurement into a spiking signal, and a spike decoder on the controller side inspired by synaptic processing to convert the received spiking signal into an analog signal. We provide design conditions on the spike decoder, the spike encoder as well as on the controller under which the closed-loop system exhibits a practical input-to-state stability property, where the adjustable parameters are the amplitudes of the spikes. The results are shown to be applicable to a class of nonlinear systems as well as to any stabilizable and detectable linear time-invariant system. Numerical simulations on a single-link manipulator illustrate the potential of the approach.
This work proposes a noisy group neuron (NGN) model, which incorporates population-level synchronous resetting and neural stochasticity as fundamental computational mechanisms, and develops the NGN method as a framework that combines the NGN model with backpropagation learning based on mean-field dynamics.
Yajie Zhai, Yanmei Kang, Meng Li et al.· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
