Optimizing computation of arithmetic operations based on a pre-trained neural network

Nowadays, the main methods of improving the performance of arithmetic operations on fixed and floating numbers are performed by multicore computer systems (CS) are the improvement of the microarchitecture of systems and algorithms implemented in compilers and libraries. Separately, it is necessary to highlight the complex tasks of implementing operations with long numbers that exceed the size of a machine word. This is due to the limited size of the processor registers and the data bus. As a result, the implementation of traditional operations, such as addition, multiplication, division, become more difficult to implement in modern computer systems. To solve this problem, there are currently several algorithms. However, the available algorithms can be quite slow, since they are sequential and implement bitwise operations on the bits of a number. In order to improve the performance of this class of calculations, a model based on a neural network is proposed. The purpose of this model is to reduce the execution time and CPU usage. A neural network model for performing arithmetic operations has been developed. A method of generating a data set based on graph traversal is proposed and tested experimentally. Algorithms for performing arithmetic operations based on a pre-trained neural network are constructed.

Authors: O. T. Mohammed, A. A. Paznikov

Direction: Informatics, Computer Technologies And Control

Keywords: neural networks, machine learning, long arithmetic operations, parallel computing

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