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Article title HARDWARE IMPLEMENTATION OF A CONVOLUTIONAL NEURAL NETWORK IN FPGA BASED ON FIXED POINT CALCULATIONS
Authors R. A. Solovyev, A. G. Kustov, V. S. Ruhlov, A. N. Shchelokov, D. V. Puzyrkov
Section SECTION IV. COMPUTER SCIENCE AND ELECTRONICS
Month, Year 07, 2017 @en
Index UDC 004.052.32
DOI
Abstract The latest research in the field of neural networks has shown that they cope well with a variety of tasks related to the classification and processing of images, audio and video data. The dimension and computational complexity during classification is so great that even powerful general-purpose CPUs cannot cope well with these computations. For high-grade work with modern neural networks powerful and therefore expensive GPU (video cards) are usually used. This is especially true for processing video information in real time. Some structures of neural networks, with very high accuracy of image classification, have properties that are easily transferred to the hardware platform. Since the requirements for hardware for working with neural networks are constantly growing, it is necessary to develop special hardware units for use in VLSI and FPGA. In this work, we propose to develop a set of hardware blocks and methods for implementing neural networks on FPGAs in order to accelerate the calculation and development of equipment for performing image classification tasks. It is assumed that at the input we already have a pre-conditioned neural network and we need to create a device that performs the classification operation. Initially, we are given both the structure and the weights of the neural network. A technique is proposed for the transition from a floating point model to a fixed-point calculation, without loss of classification accuracy. The implementation of the basic blocks of the test neural network in hardware is suggested. The results of experiments on a test convolutional neural network, trained on a set of MNIST images, are presented.

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Keywords Сonvolutional neural nets; FPGA; fixed point calculations; 2D convolution.
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