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Nanoscale devices attracted significant research effort from the industry and academia due to their operation principals being based on different physical properties which provide advantages in the design of certain classes of circuits over conventional CMOS transistors. Neuromorphic Circuits for Nanoscale Devices contains recent research papers presented in various international conferences and journals to provide insight into how the operational principles of the nanoscale devices can be utilized for the design of neuromorphic circuits for various applications of non-volatile memory, neural network training/learning, and image processing. The topics discussed in the book include:Nanoscale Crossbar Memory DesignQ-Learning and Value Iteration using Nanoscale DevicesImage Processing and Computer Vision Applications for Nanoscale DevicesNanoscale Devices based Cellular Nonlinear/Neural Networks
Pinaki Mazumder, Yalcin Yilmaz, Idongesit Ebong
Preface Acknowledgement List of Contributors List of Figures List of Tables List of Abbreviations 1 Introduction 2 Crossbar Memory Simulation and Performance Evaluation 3 Memristor Digital Memory 4 Multi-Level Memory Architecture 5 Neuromorphic Building Blocks with Memristors 6 Value Iteration with Memristors 7 Tunneling-Based Cellular Nonlinear Network Architectures for Image Processing 8 Color Image Processing with Multi-peak Resonant Tunneling Diodes 9 Design of a Velocity-Tuned Filter Using a Matrix of Resonant Tunneling Diodes 10 Image Processing by a Programmable Artificial Retina Comprising Quantum Dots and Variable Resistance Devices 11 Memristor-based Cellular Nonlinear/Neural Network: Design, Analysis and Applications 12 Dynamic Analysis of Memristor-based Neural Network and its Application Appendix Index About the Authors
