CMOS图像传感器和细胞神经网络(CNN)的高效紧凑集成,用于智能处理

Chung-Yu Wu, Wen-Cheng Yen
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引用次数: 1

摘要

利用神经元-双极结晶体管(BJT)作为光电晶体管和单晶体管神经元,可以将细胞神经网络(CNN)与CMOS图像传感器紧密集成,将光学图像直接输入到CNN中进行神经图像处理。利用n阱CMOS技术中寄生pnp BJT实现的神经元-BJT,可以在很小的芯片面积内实现具有对称模板的光输入CNN。细胞面积可小至20 /spl mu/m/spl倍/24 /spl mu/m。仿真结果证实了所提出的光输入紧凑型CNN的功能是正确的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An efficient and compact integration of CMOS image sensors and cellular neural network (CNN) for intelligent processing
By using the neuron-bipolar junction transistor (BJT) as phototransistor and single-transistor neuron, the cellular neural network (CNN) can be compactly integrated with CMOS image sensors so that the optical images can be input to the CNN directly for neural image processing. With the neuron-BJT, realized by the parasitic pnp BJT in n-well CMOS technology, the optical-input CNN with symmetric templates can be implemented in very small chip area. The cell area can be as small as 20 /spl mu/m/spl times/24 /spl mu/m. The simulation results have confirmed the correct function of the proposed optical-input compact CNN.
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