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Hardware Spiking Artificial Neurons, Their Response Function, and Noises Read chapter Read first chapter

2017 | OriginalPaper | Chapter

Memristor-Based Platforms: A Comparison Between Continous-Time and Discrete-Time Cellular Neural Networks

Authors : Young-Su Kim, Sang-Hak Shin, Jacopo Secco, Keyong-Sik Min, Fernando Corinto

Published in: Advances in Neuromorphic Hardware Exploiting Emerging Nanoscale Devices

Publisher: Springer India

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Abstract

In this chapter, theory, circuit design methodologies and possible applications of Cellular Nanoscale Networks (CNNs) exploiting memristor technology are reviewed. Memristor-based CNNs platforms (MCNNs) make use of memristors to realize analog multiplication circuits that are essential to perform CNN calculation with low power and small area.

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previous chapter Neuromemristive Systems: A Circuit Design Perspective
next chapter Reinterpretation of Magnetic Tunnel Junctions as Stochastic Memristive Devices
Footnotes
1
In Eq. 3 the cell states are noted as \(h_{i,j}\) instead if \(y_{i,j}\) since for the BPI algorithm the possible states are discrete and properly fixed. On the other hand, in the algorithm by Itoh and Chua the state of the single cell coincides with the actual internal state of the memristive element.
 
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Kim, Y.S., Min, K.S.: Synaptic weighting circuits for cellular neural networks. In: International Workshop on Cellular Nanoscale Networks and Their Applications (CNNA2012), Turin, Italy (2012)
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Metadata
Title
Memristor-Based Platforms: A Comparison Between Continous-Time and Discrete-Time Cellular Neural Networks
Authors
Young-Su Kim
Sang-Hak Shin
Jacopo Secco
Keyong-Sik Min
Fernando Corinto
Copyright Year
2017
Publisher
Springer India
Book
Advances in Neuromorphic Hardware Exploiting Emerging Nanoscale Devices

Print ISBN: 978-81-322-3701-3

Electronic ISBN: 978-81-322-3703-7

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-81-322-3703-7_4