残数系统提高卷积神经网络性能

N. Chervyakov, P. Lyakhov, M. Valueva
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引用次数: 18

摘要

本文研究了基于卷积神经网络的索贝尔滤波模式识别方法。通过MATLAB软件建模,对卷积神经网络块的参数进行了实验选择。提出了用剩余数系统构造的卷积神经网络的结构,以实现时延最小化。与使用二进制数系统相比,使用特殊类型的模块可使设备的工作速度加快37.4%,与使用已知剩余数系统实现相比,可加快18.5%。
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Increasing of convolutional neural network performance using residue number system
This paper considers the method of pattern recognition based on a convolutional neural network using Sobel filters. Parameters of the convolutional neural network blocks were chosen experimentally by software modeling in MATLAB. We presents the architecture of the convolutional neural network constructed with residue number system for delay minimization. Using of special type of modules allows to accelerate the work of the device by 37,4% as compared to using a binary number system and by 18,5% as compared to using a known residue number system realization.
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