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Comparative Study on Analog and Digital Neural Networks


Vipan Kakkar


Vol. 9  No. 7  pp. 14-21


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.


Neural Networks, Digital design, analog design, VLSI, power consumption, robustness, efficiency, applications