Memristors for Neuromorphic Circuits and Artificial Intelligence Applications

Edited by
April 2020
244 pages
  • ISBN978-3-03928-576-1 (Paperback)
  • ISBN978-3-03928-577-8 (PDF)

This book is a reprint of the Special Issue Memristors for Neuromorphic Circuits and Artificial Intelligence Applications that was published in

Chemistry & Materials Science
Physical Sciences
Artificial Intelligence (AI) has found many applications in the past decade due to the ever increasing computing power. Artificial Neural Networks are inspired in the brain structure and consist in the interconnection of artificial neurons through artificial synapses. Training these systems requires huge amounts of data and, after the network is trained, it can recognize unforeseen data and provide useful information. The so-called Spiking Neural Networks behave similarly to how the brain functions and are very energy efficient. Up to this moment, both spiking and conventional neural networks have been implemented in software programs running on conventional computing units. However, this approach requires high computing power, a large physical space and is energy inefficient. Thus, there is an increasing interest in developing AI tools directly implemented in hardware. The first hardware demonstrations have been based on CMOS circuits for neurons and specific communication protocols for synapses. However, to further increase training speed and energy efficiency while decreasing system size, the combination of CMOS neurons with memristor synapses is being explored. The memristor is a resistor with memory which behaves similarly to biological synapses. This book explores the state-of-the-art of neuromorphic circuits implementing neural networks with memristors for AI applications.
  • Paperback
License and Copyright
© 2020 by the authors; CC BY-NC-ND license
memristor; artificial synapse; neuromorphic computing; memristor-CMOS hybrid circuit; temporal pooling; sensory and hippocampal responses; cortical neurons; hierarchical temporal memory; neocortex; memristor-CMOS hybrid circuit; defect-tolerant spatial pooling; boost-factor adjustment; memristor crossbar; neuromorphic hardware; memristor; compact model; emulator; neuromorphic; synapse; STDP; pavlov; neuromorphic systems; spiking neural networks; memristors; spike-timing-dependent plasticity; RRAM; vertical RRAM; neuromorphics; neural network hardware; reinforcement learning; AI; neuromorphic computing; multiscale modeling; memristor; optimization; RRAM; simulation; memristors; neuromorphic engineering; OxRAM; self-organization maps; synaptic device; memristor; neuromorphic computing; artificial intelligence; hardware-based deep learning ICs; circuit design; memristor; RRAM; variability; time series modeling; autocovariance; graphene oxide; laser; memristor; crossbar array; neuromorphic computing; wire resistance; synaptic weight; character recognition; neuromorphic computing; Flash memories; memristive devices; resistive switching; synaptic plasticity; artificial neural network; spiking neural network; pattern recognition; strongly correlated oxides; resistive switching; neuromorphic computing; transistor-like devices; artificial intelligence; neural networks; resistive switching; memristive devices; deep learning networks; spiking neural networks; electronic synapses; crossbar array; pattern recognition