报告详情
Adaptive Neuromorphic Vision Circuits for Event-Based Intelligent Sensing
编号：69
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更新：2026-10-05 23:42:20
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摘要
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.
关键词
Neuromorphic Vision,Event-Based Sensing,Spiking Neural Network,Edge AI,Process Innovation
报告人
Wai Yie Leong
Researcher INTI International University稿件作者
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重要日期
-
会议日期
10月11日
2026
至10月14日
2026
-
12月30日 2025
报告提交截止日期
-
09月28日 2026
提前注册日期
-
10月10日 2026
初稿截稿日期
-
10月14日 2026
注册截止日期
主办单位
United Societies of Science
承办单位
Posts and Telecommunications Institute of Technology
协办单位
IEEE Section
IEEE Vietnam Section
IEEE Vietnam Section
联系方式
- ASIANComNet 2026
- as******@usssociety.org
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