Skip to main content

About this book

This book covers all major aspects of cutting-edge research in the field of neuromorphic hardware engineering involving emerging nanoscale devices. Special emphasis is given to leading works in hybrid low-power CMOS-Nanodevice design. The book offers readers a bidirectional (top-down and bottom-up) perspective on designing efficient bio-inspired hardware. At the nanodevice level, it focuses on various flavors of emerging resistive memory (RRAM) technology. At the algorithm level, it addresses optimized implementations of supervised and stochastic learning paradigms such as: spike-time-dependent plasticity (STDP), long-term potentiation (LTP), long-term depression (LTD), extreme learning machines (ELM) and early adoptions of restricted Boltzmann machines (RBM) to name a few. The contributions discuss system-level power/energy/parasitic trade-offs, and complex real-world applications. The book is suited for both advanced researchers and students interested in the field.

Table of Contents


2017 | OriginalPaper | Chapter

Hardware Spiking Artificial Neurons, Their Response Function, and Noises

Doo Seok Jeong

2017 | OriginalPaper | Chapter

Synaptic Plasticity with Memristive Nanodevices

Selina La Barbera, Fabien Alibart

2017 | OriginalPaper | Chapter

Neuromemristive Systems: A Circuit Design Perspective

Cory Merkel, Dhireesha Kudithipudi

2017 | OriginalPaper | Chapter

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

2017 | OriginalPaper | Chapter

Reinterpretation of Magnetic Tunnel Junctions as Stochastic Memristive Devices

Adrien F. Vincent, Nicolas Locatelli, Damien Querlioz

2017 | OriginalPaper | Chapter

Multiple Binary OxRAMs as Synapses for Convolutional Neural Networks

E. Vianello, D. Garbin, O. Bichler, G. Piccolboni, G. Molas, B. De Salvo, L. Perniola

2017 | OriginalPaper | Chapter

Nonvolatile Memory Crossbar Arrays for Non-von Neumann Computing

Severin Sidler, Jun-Woo Jang, Geoffrey W. Burr, Robert M. Shelby, Irem Boybat, Carmelo di Nolfo, Pritish Narayanan, Kumar Virwani, Hyunsang Hwang

2017 | OriginalPaper | Chapter

Novel Biomimetic Si Devices for Neuromorphic Computing Architecture

U. Ganguly, Bipin Rajendran

2017 | OriginalPaper | Chapter

Exploiting Variability in Resistive Memory Devices for Cognitive Systems

Vivek Parmar, Manan Suri

2017 | OriginalPaper | Chapter

Theoretical Analysis of Spike-Timing-Dependent Plasticity Learning with Memristive Devices

Damien Querlioz, Olivier Bichler, Adrien F. Vincent, Christian Gamrat

2017 | CompoundObjectErratum | Chapter

Erratum to: Novel Biomimetic Si Devices for Neuromorphic Computing Architecture

U. Ganguly, Bipin Rajendran
Additional information