OPERATIONAL BASIS OF ARTIFICIAL NEURAL NETWORKS AND EVALUATION OF HARDWARE CHARACTERISTICS FOR ITS IMPLEMENTATION
Authors:   Lviv Polytechnic National University, Lviv, Ukraine, Tsmots I. G. , Opotyak Yu. V. ,   Lviv Polytechnic National University, Lviv, Ukraine, Shtohrinets B. V. ,   Lviv Polytechnic National University, Lviv, Ukraine, Mamchur T. B. ,   Lviv Polytechnic National University, Lviv, Ukraine, Oliinyk O. O. ,   Lviv Polytechnic National University, Lviv, Ukraine
Published: January 2025

The tasks performed by the intelligent components of mobile robotic systems (MRS) are analyzed and their features are determined. The operational basis for the implementation of hardware accelerators of artificial neural networks (ANN) is defined and divided into three groups of neurooperations: preprocessing, processing and calculation of transfer functions. It is shown that the operations of the first group provide the transformation of the input data to the form that will give the best results, the operations of the second group (multiplication, addition, group summation, calculation of the dot product, calculation of a two-dimensional convolution, multiplication of the matrix by a vector) are performed directly in the neural network itself in the process of training and functioning, operations of the third group provide calculation of transfer functions. It is determined that the specialized hardware of the intelligent components of the MRS should provide real-time operation and take into account the limitations in terms of dimensions and power consumption. It is proposed to carry out the development of specialized hardware of intelligent components of the MRS on the basis of an integrated approach, which covers the capabilities of the modern element base, parallel methods of data processing, algorithms and structures of hardware and takes into account the requirements of specific applications. For the development of hardware accelerators ANN, the following principles were chosen: modularity; homogeneity and regularity of the structure; localization and reduction of the number of connections between elements; pipeline and spatial parallelism; coordination of intensities in the receipt of input data, calculation and issuance of results; specialization and adaptation of hardware structures to algorithms for the implementation of neurooperations. It is proposed to use the following characteristics to evaluate specialized hardware: hardware resources, operation time and equipment utilization efficiency. Analytical expressions and a simulation model for evaluating the characteristics of specialized hardware have been developed, the results of which are used to select the most effective accelerator and elemental structure for the implementation of intelligent components of the MRS. The method of selection of the element base for the implementation of intelligent components of the MRS has been improved, which, by taking into account the results of the assessment of the characteristics of hardware accelerators, the requirements of a specific application and the existing element base for their implementation, ensures the selection of the most effective of the existing ones.