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

  • Young-Su Kim
  • Sang-Hak Shin
  • Jacopo Secco
  • Keyong-Sik Min
  • Fernando Corinto
Part of the Cognitive Systems Monographs book series (COSMOS, volume 31)


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.


Cellular Automaton Cellular Automaton Input Current Synaptic Weight Lena Image 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



The first part of this work was financially supported by NRF-2011-0030228, NRF-2013K1A3A1A25038533, NRF-2013R1A1A2A10064812, and BK Plus with the Educational Research Team for Creative Engineers on Material-Device-Circuit Co-Design (Grant No: 22A20130000042), funded by the National Research Foundation of Korea (NRF), and by Global Scholarship Program for Foreign Graduate Students at Kookmin Univ. The CAD tools were supported by IC Design Education Center (IDEC), Daejeon, Korea.

The second part of this work has been supported by the Ministry of Foreign Affairs Con il contributo del Ministero degli Affari Esteri, Direzione Generale per la Promozione del Sistema Paese.


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Copyright information

© Springer (India) Pvt. Ltd. 2017

Authors and Affiliations

  • Young-Su Kim
    • 1
  • Sang-Hak Shin
    • 1
  • Jacopo Secco
    • 1
  • Keyong-Sik Min
    • 1
  • Fernando Corinto
    • 1
  1. 1.Politecnico di TorinoTorinoItaly

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