Evaluation of CNN template robustness towards VLSI implementation

P. Kinget, M. Steyaert
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引用次数: 19

Abstract

In this paper a method for the evaluation of the static robustness of cellular neural network (CNN) templates is proposed. From this evaluation the circuit accuracy specifications for a VLSI implementation can be derived which allows the designer to optimize the performance. Moreover, from this evaluation method guidelines for robust template designs can be derived and parameter testing templates can be developed.<>
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CNN模板对VLSI实现的鲁棒性评估
提出了一种评价细胞神经网络模板静态鲁棒性的方法。从这个评估中,可以推导出VLSI实现的电路精度规格,使设计人员能够优化性能。此外,从该评价方法可以导出稳健模板设计的指导方针,并可以开发参数测试模板。
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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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