Exo-intelligent Data-Driven Reconfigurable Computing Platform

Vladimir Zaborovskij (), Alexander Antonov and Igor Kaliaev
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Vladimir Zaborovskij: Peter The Great St. Petersburg Polytechnic University
Alexander Antonov: Peter The Great St. Petersburg Polytechnic University
Igor Kaliaev: South Federal University

A chapter in Digital Transformation and the World Economy, 2022, pp 181-203 from Springer

Abstract: Abstract The key concept of the digital age is based on the Turing machine abstraction, which defines computational processes as the evolution of the states of a machine that performs a basic set of computational operations (BSCO) step by step. Based on Ludwig Boltzmann’s statement that “available energy is the main object at stake in the struggle for the evolution of the world,” the article discusses the possibility of creating a heterogeneous computing platform using specialized hardware to perform a basic set of operations that reduce costs energy for algorithm implementation. The platform being developed has a certain entropy potential in relation to possible options for hardware and software configurations of the computational structure and composition of the BSCO, which is formed using so-called basic computational equivalent (BCE), which can build on standard universal multicore processors (CPU), GPU accelerators or FPGA-based reconfigurable coprocessors. FPGA configuration files are organized into a specialized knowledge base that is constantly updated using machine learning techniques that are used to target computational platform reconfiguration and meet different requirements to algorithms implementation.

Keywords: Computer technology; Artificial intelligence; Machine learning; Exo-intelligence; Cognitive functions (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/978-3-030-89832-8_10

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