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Analog Learning Neural Circuit with Switched Capacitor and the Design of Deep Learning Model

  • Masashi Kawaguchi
  • Naohiro Ishii
  • Masayoshi Umeno
Chapter
Part of the Studies in Computational Intelligence book series (SCI, volume 726)

Abstract

In the neural network field, many application models have been proposed. Previous analog neural network models were composed of the operational amplifier and fixed resistance. It is difficult to change the connecting weight of network. In this study, we used analog electronic multiple and switched capacitor circuits. The connecting weights describe the input voltage. It is easy to change the connection coefficient. This model works only on analog electronic circuits. It can finish the learning process in a very short time and this model will enable more flexible learning. However, the structure of this model is only one input and one output network. We improved the number of unit and network layers. Moreover, we suggest the possibility of the realization of the hardware implementation of the deep learning model.

Keywords

Electronic circuit Neural network Multiple circuit Deep learning 

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Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Masashi Kawaguchi
    • 1
  • Naohiro Ishii
    • 2
  • Masayoshi Umeno
    • 3
  1. 1.Department of Electrical & Electronic EngineeringSuzuka National College of TechnologyShirokoJapan
  2. 2.Department of Information ScienceAichi Institute of TechnologyToyotaJapan
  3. 3.Department of Electronic EngineeringChubu UniversityKasugai, AichiJapan

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