Reconfigurable Modular Computing System

Abstract

Purpose of research. A reconfigurable computer system consists of a computing system and special-purpose computers that are used to solve the tasks of vector and matrix algebra, pattern recognition. There are distinctions between matrix and associative systems, neural networks. Matrix computing systems comprise a set of processor units connected through a switching device with multi-module memory. They are designed to solve vector, matrix and data array problems. Associative systems contain a large number of operating devices that can simultaneously process multiple data streams. Neural networks and neurocomputers have high performance when solving problems of expert systems, pattern recognition due to parallel processing of a neural network.Methods. An information graph of the computational process of a reconfigurable modular system was plotted. Structural and functional schemes, algorithms that implement the construction of specialized modules for performing arithmetic and logical operations, search operations and functions for replacing occurrences in processed words were developed. Software for modelling the operation of the arithmetic-symbol processor, specialized computing modules, and switching systems was developed.Results. A block diagram of a reconfigurable computing modular system was developed. The system consists of compatible functional modules and is capable of static and dynamic reconfiguration, has a parallel connection structure of the processor and computing modules through the use of interface channels. It consists of an arithmeticsymbol processor, specialized computing modules and switching systems; it performs specific tasks of symbolic information processing, arithmetic and logical operations.Conclusion. Systems with a reconfigurable structure are high-performance and highly reliable computing systems that consist of integrated processors in multi-machine and multiprocessor systems. Reconfigurability of the structure provides high system performance due to its adaptation to computational processes and the composition of the processed tasks.