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引用次数: 21

摘要

报道了一种可编程细胞神经网络(CNN)的实验单片实现。它克服了CMOS VLSI技术固有的一些特性和限制,并允许通过模块化连接CNN芯片和适度数量的单元来构建任意大的连续时间模拟CNN。模板值是逐步可编程的,根据功能选择值,而不是根据传统的二进制权重。所有外部输入、输出和控制信号都是电气和数字的,因此CNN可以直接连接到控制器。设计采用1微米n阱CMOS技术。每个电池占用0.4 mm/sup 2/,包括所有支持电路;为了便于电路测试,每个芯片只集成了一个单元。图中显示了由16个单细胞芯片连接而成的4/spl次/4 CNN原型的测量CNN瞬态。主要的预期应用是声学信号的处理和算法的开发。
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A programmable, modular CNN cell
An experimental monolithic implementation of a programmable cellular neural network (CNN) is reported. It overcomes some of the characteristics and restrictions inherent in CMOS VLSI technologies, and allows an arbitrarily large continuous-time analog CNN to be built up by modularly connecting CNN chips with a modest number of cells. The template values are step-wise programmable, with values chosen for functionality rather than according to conventional binary weighting. All external input, output and control signals are electrical and digital, so the CNN can be directly connected to a controller The design was carried out in a 1-micron n-well CMOS technology. Each cell occupies 0.4 mm/sup 2/, including all support circuitry; only one cell per chip was integrated in order to facilitate circuit testing. Measured CNN transients from a prototype 4/spl times/4 CNN, formed by connecting 16 one-cell chips are shown. The principal intended applications are the processing of acoustical signals and algorithm development.<>
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Realisation of a digital cellular neural network for image processing Convergence and stability of the FSR CNN model A versatile CMOS building block for fully analogically-programmable VLSI cellular neural networks A fast, complex and efficient test implementation of the CNN Universal Machine Optoelectronic cellular neural networks based on amorphous silicon thin film technology
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