Comparative Study on Analog and Digital Neural Networks
Comparative Study on Analog and Digital Neural Networks
复制标题
模拟和数字神经网络的比较研究
DOI：
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发表时间：
2009
期刊：
影响因子：
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通讯作者：
Vipan Kakkar
中科院分区：
文献类型：
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作者：
Vipan Kakkar
在过去的二十年里，人们对神经网络进行了大量的研究，产生了许多类型的神经网络。这些神经网络可以以多种方式实现。由于对神经网络研究兴趣的复苏，VLSI取得了一些重要的技术进展。本文对神经网络的模拟实现和数字实现进行了比较研究。讨论的主题分别包括这些实现技术的功耗、面积、健壮性和实现效率。它可以估计模拟或数字神经网络是否适合特定应用。观察到模拟和数字神经网络之间的选择取决于应用。目标是估计哪种类型的实现应该用于哪类应用程序。本工作是在研究仅局限于模式分类的神经网络实现的基础上，重点研究广泛应用的分层前馈神经网络。
Summary For the last two decades, lot of research has been done on neural networks, resulting in many types of neural networks. These neural networks can be implemented in number of ways. Due to the revival of research interest in neural networks, some important technological developments have been made in VLSI. This paper discusses comparative study between analog implementation and digital implementation for neural networks. The discussion topics include power-consumption, area, robustness, and implementation efficiency of these implementation techniques respectively. It can be estimated whether an analog or digital neural network is optimum for a specific application. It is observed that the choice between analog and digital neural networks is application dependent. The goal is to estimate which type of implementation should be used for which class of applications. This work is based on the study of neural implementations restricted only to pattern classification and focuses on widely used layered feed-forward neural network.