Effective mapping of artificial neural network algorithms onto massively parallel hardware: the REMAP programming environment

Author

Li, Guang ; Svensson, Bertil

Author_Institution

Dept. of Comput. Eng., Chalmers Univ. of Technol., Goteborg, Sweden

Volume

2

fYear

1995

fDate

19-21 Apr 1995

Abstract

The application of artificial neural networks (ANN) in real-time embedded systems demands high performance computers. Miniaturized massively parallel architectures are suitable computation platforms for this task. An important question which arises is how to establish an effective mapping from ANN algorithms to hardware. In this paper, we demonstrate how an effective mapping can be achieved with our programming environment in close combination with an optimized architecture design targeted for neuro-computing

Keywords

computer aided software engineering; neural net architecture; parallel architectures; programming environments; real-time systems; reconfigurable architectures; Remap programming environment; artificial neural network algorithms; effective mapping; massively parallel hardware; miniaturized massively parallel architectures; neuro-computing; optimized architecture design; programming environment; real-time embedded systems; Application software; Artificial neural networks; Computer networks; Concurrent computing; Embedded computing; Embedded system; Hardware; High performance computing; Parallel architectures; Real time systems;

fLanguage

English

Publisher

ieee

Conference_Titel

Algorithms and Architectures for Parallel Processing, 1995. ICAPP 95. IEEE First ICA/sup 3/PP., IEEE First International Conference on

Conference_Location

Brisbane, Qld.

Print_ISBN

0-7803-2018-2

Type

conf

DOI

10.1109/ICAPP.1995.472292

Filename

472292