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Politechniki Koszalińskiej

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Configurable microcontroller array / O. Masliennikov, J. Shevtshenko, A. Sergyienko.

Autor: Rodzaj materiału: ArtykułJęzyk: angielski Praca zawiera:
  • International Conference on Parallel Computing in Electrical Engineering PARELEC 2002 (2002 ; Warszawa, Polska) PARELEC 2002
Rodzaj/forma: W: Proceedings International Conference on Paralel Computing in Electrical Engineering IEEE. - Los Alamitos : 2002, IEEE. - s.47-49Streszczenie: In this paper, the configurable microcontroller array based on the i8051 processor unit (PU) architecture is proposed. The use of well-known PU architecture simplifies the application programming. The designed microcontroller PU core has in 6 times higher instruction implementation speed, and in more than 2.5 times clock frequency than the orginal microcontroller. The proposed techniqu of mapping the program into configurable hardware showed the 1.5-2 - fold hardware minimization. It shows an effective way to speedup the implementation of both computing and control intensive algorithms. Proposed array is very useful in such applications, where logic intensive calculations, or high speed byte handling computations are of demand. For example, such applications are homomorphic image processing, pattern recognition, genetic algorithms, neural nets, etc.
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In this paper, the configurable microcontroller array based on the i8051 processor unit (PU) architecture is proposed. The use of well-known PU architecture simplifies the application programming. The designed microcontroller PU core has in 6 times higher instruction implementation speed, and in more than 2.5 times clock frequency than the orginal microcontroller. The proposed techniqu of mapping the program into configurable hardware showed the 1.5-2 - fold hardware minimization. It shows an effective way to speedup the implementation of both computing and control intensive algorithms. Proposed array is very useful in such applications, where logic intensive calculations, or high speed byte handling computations are of demand. For example, such applications are homomorphic image processing, pattern recognition, genetic algorithms, neural nets, etc.

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