Feedback

X

Memristors for Neuromorphic Circuits and Artificial Intelligence Applications

en

0 Ungluers have Faved this Work
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.

This book is included in DOAB.

Why read this book? Have your say.

You must be logged in to comment.

Rights Information

Are you the author or publisher of this work? If so, you can claim it as yours by registering as an Unglue.it rights holder.

Downloads

This work has been downloaded 405 times via unglue.it ebook links.
  1. 380 - pdf (CC BY-NC-ND) at Unglue.it.

Keywords

  • AI
  • artificial intelligence
  • artificial neural network
  • artificial synapse
  • autocovariance
  • boost-factor adjustment
  • character recognition
  • circuit design
  • compact model
  • cortical neurons
  • crossbar array
  • deep learning networks
  • defect-tolerant spatial pooling
  • electronic synapses
  • emulator
  • Flash memories
  • graphene oxide
  • hardware-based deep learning ICs
  • hierarchical temporal memory
  • laser
  • memristive devices
  • memristor
  • memristor crossbar
  • memristor-CMOS hybrid circuit
  • memristors
  • multiscale modeling
  • Neocortex
  • neural network hardware
  • neural networks
  • Neuromorphic
  • neuromorphic computing
  • neuromorphic engineering
  • neuromorphic hardware
  • neuromorphic systems
  • neuromorphics
  • optimization
  • OxRAM
  • pattern recognition
  • pavlov
  • reinforcement learning
  • resistive switching
  • RRAM
  • self-organization maps
  • sensory and hippocampal responses
  • simulation
  • spike-timing-dependent plasticity
  • Spiking Neural network
  • spiking neural networks
  • STDP
  • strongly correlated oxides
  • synapse
  • synaptic device
  • synaptic plasticity
  • synaptic weight
  • temporal pooling
  • time series modeling
  • transistor-like devices
  • variability
  • vertical RRAM
  • wire resistance

Links

DOI: 10.3390/books978-3-03928-577-8

Editions

edition cover
edition cover

Share

Copy/paste this into your site: