Parallel Implementations of Hopfield Neural Networks On GPU

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Parallel Implementations of Hopfield Neural Networks On GPU Li LIANG August 12, 2011 Abstract In recent years the multi-cores and General-Purpose GPU (GPGPU) architectures have become general platforms for various of parallel appli- cations, with lots of parallel algorithms being proposed for this interesting persperctive. In this report, we study and develop a particular kind of arti- ficial neural network (ANN), in hopfield model, to solve some optimization problems, since it has a highly parallel nature. Section 1 introduces the context of the problem and the linked topics of this internship. In Section 2 we synthesize some previous work in this domain. In section 3 we study the parallel mechanism and propose the algorithm to realize this neural net- work. Section 4 interprets the method we use to map the neural network structure. Section 5 discusses the implementation details of simulations of hopfield networks on GPU. In section 6, we comment on the results and the performances compared with the CPU implementation. Contents 1 Introduction 2 1.1 Context . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 1.2 Problem Description . . . . . . . . . . . . . . . . . . . . . . . .