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Using on-line arithmetic and reconfiguration for neuroprocessor implementation

  • Artificial Neural Nets Simulation and Implementation
  • Conference paper
  • First Online:
Engineering Applications of Bio-Inspired Artificial Neural Networks (IWANN 1999)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1607))

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Abstract

Artificial neural networks can solve complex problems such as time series prediction, handwritten pattern recognition or speech processing. Though software simulations are essential when one sets about to study a new algorithm, they cannot always fulfill real-time criteria required by some practical applications. Consequently, hardware implementations are of crucial import.

The appearance of fast reconfigurable FPGA circuits brings about new paths for the design of neuroprocessors. A learning algorithm is divided into different steps that are associated with specific FPGA configurations. The training process then consists of alternating computing and reconfiguration stages. Such a method leads to an optimal use of hard-ware resources.

This paradigm is applied to the design of a neuroprocessor implementing multilayer perceptrons with on-chip training and pruning. All arithmetic operations are carried out with on-line operators. We also describe the principles of the hardware architecture, focusing in particular on the pruning mechanisms.

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Authors and Affiliations

  1. Logic Systems Laboratory, Swiss Federal Institute of Technology, CH-1015, Lausanne, Switzerland

    Jean-Luc Beuchat & Eduardo Sanchez

Authors
  1. Jean-Luc Beuchat
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  2. Eduardo Sanchez
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José Mira Juan V. Sánchez-Andrés

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© 1999 Springer-Verlag Berlin Heidelberg

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Beuchat, JL., Sanchez, E. (1999). Using on-line arithmetic and reconfiguration for neuroprocessor implementation. In: Mira, J., Sánchez-Andrés, J.V. (eds) Engineering Applications of Bio-Inspired Artificial Neural Networks. IWANN 1999. Lecture Notes in Computer Science, vol 1607. Springer, Berlin, Heidelberg . https://doi.org/10.1007/BFb0100479

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  • DOI: https://doi.org/10.1007/BFb0100479

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-66068-2

  • Online ISBN: 978-3-540-48772-2

  • eBook Packages: Springer Book Archive

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