Design Of Memristor-based Spiking Neural Network And Its Application In Image Classification

Spiking neural network(SNN),a reference to biological neural network in a deeper level,is regarded as the third generation of artificial neural network.SNNs have a real-time,asynchronous and efficient computing power and a greater information capacity.It is hopeful to help human reach a higher level of artificial intelligence.On the other hand,nanomemristor devices have many advantages,such as non-volatile resistance,high life length,stackable,extensible and CMOS-compatible.There is a possibility to break through the bottleneck of existing microelectronics technology with the memristor,which also bring a new thinking for the circuit implementation of artificial neural networks.In this thesis,the application of memristor-SNNs in image classification is explored based on the combination of memristor and SNN.A general model with adaptive drift rate and threshold is given through improving an existing model.This given model could more accurately represent the physical characteristics of some real memristors than others.The model parameters of the memristors used for neurons and synapses are adapted respectively.The STDP learning circuit is designed by combining the provided memristor-based leaky integrate and fire(MLIF)model and synaptic memristor and extended to a crossbar structure.The corresponding winner-take-all(WTA)circuit is constructed.On the basis of reflecting the circuit characteristics as much as possible,the designed circuit before is abstracted into a relative simple model for designing the memristor-SNN in software environment.At the same time,other design details and the learning process are discussed attentively.The designed memristor-SNN is applied to image classification tasks,including twoclassification and multi-classification.The results are analyzed and the comparison with other existing SNNs is given.The work of this thesis include an improved with-threshold drift rate adaptive memristor model,a MLIF circuit,a memristor-based STDP circuit and a WTA circuit.On this basis,a memristor-based SNN is designed for image classification in software environment.In this thesis,the pure hardware implementation of SNNs is explored.Corresponding circuits representing parts of SNN is constructed considering saving circuit cost as much as possible.The designed memristor-based SNN in software environment has certain advantages compared with others.