USING FAST PROTOTYPING FACILITIES FOR IMPLEMENTATION OF A CONVOLUTION NEURAL NETWORK ON A FPGA

Abstract

Research in the field of artificial intelligence is carried out with increasing interest every year. The fields of application of artificial intelligence are quite extensive: automation, analysis of a large amount of data, smart home technology, machine vision, etc. Artificial intelligence technologies are based on the use of artificial neural networks, which are based on the principles of the animal nervous system. In this case, the actual issue is the implementation of artificial neural networks on various software and hardware platforms: programmable logic integrated circuits of the FPGA type (Field Programmable Gate Array), on special purpose integrated circuits (Application- Specific Integrated Circuit, ASIC), GPU, CPU etc. FPGA performs best in low-power mobile systems. ASIC demonstrates the highest performance at a fairly high development cost. The problem of rapid prototyping of projects based on the use of artificial neural networks for FPGAs using conventional methods (using HDL languages, HDL encoders, graphic programming) is that either such a project is complex and time-consuming to debug (HDL languages), or the resulting code is not optimal (HDL encoders), or the duration of the project development and the complexity of reconfiguring the neural network (graphical programming) are high. Therefore, in the framework of this work, an effective method for designing fully connected and convolutional neural networks for their implementation on FPGAs using the Xilinx System Generator for DSP and Matlab / Simulink package is considered. Artificial neural networks generated in this way are easily reconfigurable and allow solving the following problems: image recognition, optimal filtering (for example, for problems of subsurface radar).