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Memristors for Neuromorphic Circuits and Artificial Intelligence Applications

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...

Ausführliche Beschreibung

Gespeichert in:
1. Verfasser: Suñé, Jordi
Format: Online
Sprache:Englisch
Veröffentlicht: MDPI - Multidisciplinary Digital Publishing Institute 2021
Schlagworte:
TA1-2040
T1-995
graphene oxide
artificial neural network
simulation
neural networks
STDP
neuromorphics
spiking neural network
artificial intelligence
hierarchical temporal memory
synaptic weight
optimization
transistor-like devices
multiscale modeling
memristor crossbar
spike-timing-dependent plasticity
memristor-CMOS hybrid circuit
pavlov
wire resistance
AI
neocortex
synapse
character recognition
resistive switching
electronic synapses
defect-tolerant spatial pooling
emulator
compact model
deep learning networks
artificial synapse
circuit design
memristors
neuromorphic engineering
memristive devices
OxRAM
neural network hardware
sensory and hippocampal responses
neuromorphic hardware
boost-factor adjustment
RRAM
variability
Flash memories
neuromorphic
reinforcement learning
laser
memristor
hardware-based deep learning ICs
temporal pooling
self-organization maps
crossbar array
pattern recognition
strongly correlated oxides
vertical RRAM
autocovariance
neuromorphic computing
synaptic device
cortical neurons
time series modeling
spiking neural networks
neuromorphic systems
synaptic plasticity
thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBX History of engineering and technology
Online-Zugang:45996
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Zusammenfassung: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.

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