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首页> 外文期刊>Circuits and Systems I: Regular Papers, IEEE Transactions on >A Neuron-MOS-Based VLSI Implementation of Pulse-Coupled Neural Networks for Image Feature Generation
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A Neuron-MOS-Based VLSI Implementation of Pulse-Coupled Neural Networks for Image Feature Generation

机译：基于神经元-MOS的脉冲耦合神经网络的VLSI实现图像特征生成

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摘要

An analog circuit for implementing pulse-coupled neural networks (PCNNs) in very-large-scale integration (VLSI) hardware has been developed using the Neuron-MOS $(nu {rm MOS})$ technology. PCNNs are biologically inspired models having powerful ability for image feature generation. With the $nu {rm MOS}$ technology, weighted sum of multiple input signals, which is an essential of PCNNs, is implemented simply by the capacitive coupling effect in a $nu {rm MOS}$ block. By employing the switched floating gates in the $nu {rm MOS}$ blocks as temporary analog memories, the storage of image data is simply realized. Moreover, the function of decay generation, which is crucial for emulating PCNNs neuronal dynamics, is also merged into a $nu {rm MOS}$ block by utilizing the input-terminal capacitors in it. With such techniques, the circuit achieves a purely voltage-mode implementation of PCNNs in a compact structure. Inheriting the merits of PCNNs, the circuit has good discriminability against different patterns as well as robustness against rotation and translation of identical patterns, which is analogous to human image perception. The performance of the circuit has been verified by the measurements of a proof-of-concept chip fabricated in a 0.35- $mu{hbox {m}}$ double-polysilicon CMOS technology.
机译：已经使用Neuron-MOS（nu {rm MOS}）技术开发了一种用于在超大规模集成（VLSI）硬件中实现脉冲耦合神经网络（PCNN）的模拟电路。 PCNN是受生物学启发的模型，具有强大的图像特征生成能力。使用$ nu {rm MOS} $技术，只需通过$ nu {rm MOS} $块中的电容耦合效应即可实现PCNN必不可少的多个输入信号的加权和。通过将$ nu {rm MOS} $块中的开关浮动栅极用作临时模拟存储器，可以简单地实现图像数据的存储。此外，对于模拟PCNNs神经元动力学至关重要的衰变生成功能，也可以通过利用其中的输入端电容器而合并到一个nu块中。利用这种技术，该电路以紧凑的结构实现了PCNN的纯电压模式实现。电路继承了PCNN的优点，对不同模式具有良好的可分辨性，对相同模式的旋转和平移具有鲁棒性，这与人的图像感知类似。电路的性能已通过测量以0.35-μm双多晶硅CMOS技术制造的概念验证芯片得到验证。

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