Abstract
The rapid development of artificial intelligence (AI), big data analytics, cloud computing, and Internet of Things applications expect the emerging memristor devices and their hardware systems to solve massive data calculation with low power consumption and small chip area. This paper provides an overview of memristor device characteristics, models, synapse circuits, and neural network applications, especially for artificial neural networks and spiking neural networks. It also provides research summaries, comparisons, limitations, challenges, and future work opportunities.
Highlights
Resistance, capacitance and inductance are the three basic circuit components in passive circuit theory
This paper provides an overview of memristor device characteristics, models, synapse circuits, and neural network applications, especially for artificial neural networks and spiking neural networks
Thanks to its powerful computing and storage capability, a memristor is a promising device for processing tremendous data and increasing the data processing efficiency in neural networks for artificial intelligence (AI) applications (Jeong and Shi, 2018)
Summary
Resistance, capacitance and inductance are the three basic circuit components in passive circuit theory. Low power operation, non-volatile features, and small on-chip area, memristors are good candidates for artificial synaptic devices to mimicking the LTP, LTD, and STDP behaviors (Jo et al, 2010; Ohno et al, 2011; Kim et al, 2015; Wang et al, 2017; Yan et al, 2017). Single memristor synapse (1M) crossbar arrays in neural networks have the lowest complexity and low power dissipation.
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