Adaptive Neuromorphic Vision Circuits for Event-Based Intelligent Sensing
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Updated time:2026-10-05 23:42:20
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Abstract
Conventional frame-based vision systems generate redundant data, consume significant power, and suffer from high latency when operating in dynamic environments. Event-based vision sensors, inspired by the biological retina, address these limitations by asynchronously transmitting only pixel-level intensity changes. Combined with neuromorphic computing, event-driven sensing enables ultra-low-power and low-latency perception for intelligent systems. This paper presents an adaptive neuromorphic vision circuit architecture for event-based intelligent sensing applications. The proposed framework integrates Dynamic Vision Sensors (DVS), adaptive spike encoding circuits, neuromorphic processing elements, and Spiking Neural Networks (SNNs) to achieve real-time visual perception with reduced computational overhead. Performance analysis demonstrates substantial improvements in energy efficiency, latency reduction, and data bandwidth utilization compared with conventional frame-based architectures. Results indicate that the proposed system achieves over 90% lower power consumption and sub-millisecond response times while maintaining high object detection accuracy. The proposed architecture is suitable for autonomous robotics, intelligent transportation systems, surveillance, industrial automation, and next-generation edge AI platforms.
Keywords
Neuromorphic Vision,Event-Based Sensing,Spiking Neural Network,Edge AI,Process Innovation
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