Memristive Multilayer Neural Network Design And Application

Artificial neural network has been developed for many years.Up to now,it is increasingly mature.The hardware implementation of the artificial neural network is expected to realize the structure of non-Von neumann computer system.As the limit of the traditional transistor technology in Von Neumann computer system,the area of the transistor-based integrated circuits has been reduced to its minimum size and has become increasing difficult to meet the Moore's law.More and more efforts have been invested in the research of new electronic devices to replace the transistor.One of the most promising candidates is the memristor,which has several advantages such as non-volatility,high density,low power,and good scalability.Memristor are mostly utilized in developing new memory and neuromorphic computing system.The realization of memristive on-chip singer layer neural networks makes it possible to change the way that the computers proceed the data and build the non-Von Neumann computer system.However,the design of memristive multilayer neural network is still a problem,which can be used in logic operation,image processing,and pattern recognition.In this dissertation,a new memristor model is build based on the experimental data of the memristor made of a certain material,which can match the I-V curves of both sinusoidal and repetitive sweeping inputs and the changes of the memristances by voltage pulses.Using behavior-level modeling,parameters are fit to match the characteristics of the real devices.Moreover,the parameters of the model are verified and optimized.The proposed model can therefore simulate memristors made of different materials.A single memristor-based synaptic array is presented,where both plus-polarity and minus-polarity connection matrices are realized by a single crossbar array and a simple constant-term circuit.The memristance of memristor can be adjusted continuously by the applied voltage pulse numbers,the synaptic weight can therefore be stored by one memristive synapse.Considering the error factors in each step of the weight adjustment,the memristive neural network is built with high noisy tolerance.In addition,the neural network algorithm is realized on chip,and several algorithms can be considered,such as back propagation(BP)algorithm,winner-take-all algorithm,and random adjustment algorithm.Different memristive neural networks are build based on different algorithms.The applications of logic operation and pattern recognition can be realized on chip by applying the learning algorithms in the memristive neural networks.In this dissertation,four aspects of design and applications of memristor-based multilayer neural network are deeply studied.The primary innovation and research results are as follows:(1)A new memristive model is build based on the experimental data of the recent memristive device,which can match the intrinsic characteristics of the memrisoter.The proposed model can also simulate memristors made of different materials;(1)A new memristive neural synapse is designed by making full use of the memory characteristic and the adjustable memristance of the memristor,which has the advantages of smaller area,lower power,no sneak currents,and continuous correct weight adjustments;(3)Memristive multilayer neural network with good error tolerance and stability is designed by making use of the memristive characteristics of non-volatility,nano-scale size,and low power.(4)The multilayer neural network algorithm is designed based on the synaptic circuit,which is the fundamental of the applications of the memristive multilayer neural networks.The logic operation and pattern recognition is realized in the memristive multilayer neural network with high error tolerance.The learning and training are proceeded correctly,considering the error factors,which has the advantages of faster training speed,smaller learning error,and better noisy tolerance.The research results of this dissertation can make foundation for hardware implementation of larger multilayer neural network circuits,achieving more complex and practical functions,such as the facial recognition in image recognitions.Machine learning and deep learning algorithm theory can be applied to the memristor-based neural network hardware,realizing functions in the field of computer vision,speech recognition,natural language processing,and other related areas.