Analysis of the Memristor-Based Crossbar Synapse for Neuromorphic Systems.

Journal: Journal of nanoscience and nanotechnology
Published Date:

Abstract

In this study, we analyzed the memristor device typically used as a synapse in neuromorphic architecture and confirmed that the synaptic memristor device can be adopted to perform the machine learning algorithm. The nonlinear characteristics of the memristor complicates its use as the neuromorphic hardware in an artificial neural network (ANN) with a back-propagation algorithm. Using a memristor device with a nonlinear characteristic, we demonstrated that pattern classification can be implemented in ANNs using the Guide training algorithm without back-propagation. Furthermore, the memristor characteristics required to achieve accurate learning results are analyzed.

Authors

  • Bokyung Kim
    Department of Electronic and Electrical Engineering, Ewha Womans University, 11-1 Daehyun-Dong, Seodaemoon-Gu, Seoul 03760, Republic of Korea.
  • Sumin Jo
    Department of Electronic and Electrical Engineering, Ewha Womans University, 11-1 Daehyun-Dong, Seodaemoon-Gu, Seoul 03760, Republic of Korea.
  • Wookyung Sun
    Department of Electronic and Electrical Engineering, Ewha Womans University, 11-1 Daehyun-Dong, Seodaemoon-Gu, Seoul 03760, Republic of Korea.
  • Hyungsoon Shin
    Department of Electronic and Electrical Engineering, Ewha Womans University, 11-1 Daehyun-Dong, Seodaemoon-Gu, Seoul 03760, Republic of Korea.