Memristive Synapses for Brain-Inspired Computing

Although the structure and function of the human brain are still far from being fully understood, brain-inspired computing architectures mainly consisting of artificial neurons and artificial synapses have been attracting more and more attentions due to their powerful computing capability and energy efficient operation. Synaptic plasticity is believed to be the origin of learning and memory. However, it is still a big challenge to realize artificial synapses with high reliability, good scalability, and low energy consumption, comparable to their biological counterparts. The memristor is a two-terminal electronic device whose conductance can be reversibly regulated by electric stimuli. Memristive devices are considered ideal synaptic emulators due to their superior performance such as high speed and low power operation. This work reviews the recent advances in the development of memristive synapses based on different types of memristors. First, various working mechanisms of memristive synapses are discussed and compared. Then, different integration approaches of synaptic devices are described and compared. Various cognitive functions implemented with synaptic crossbar circuits are also described. Finally, the approaches for optimizing the performance parameters of memristive synapses and challenges to integrate the synaptic devices with complementary metal oxide semiconductor (CMOS) or memristive neurons are overviewed and discussed briefly.