Abstract
All modern microprocessors offer multimedia extensions that can accelerate many applications based on vector and matrix operations. In this paper the suitability of such units for the fast simulation of RBF networks is analyzed. It is shown that the reduced arithmetic precision is sufficient for recognition and training if rounding is supported. An experimental performance study revealed a high speedup in the range from 2 to 10 compared to sequential implementations.
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© 2001 Springer-Verlag Berlin Heidelberg
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Strey, A., Bange, M. (2001). Accelerating RBF Network Simulation by Using Multimedia Extensions of Modern Microprocessors. In: Dorffner, G., Bischof, H., Hornik, K. (eds) Artificial Neural Networks — ICANN 2001. ICANN 2001. Lecture Notes in Computer Science, vol 2130. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44668-0_23
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DOI: https://doi.org/10.1007/3-540-44668-0_23
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