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Reconfigurable hardware for neural networks: binary versus stochastic

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Abstract

This paper is focused on hardware implementation of neural networks. We propose a reconfigurable, low-cost and readily available hardware architecture for an artificial neuron. For this purpose, we use field-programmable gate arrays i.e. FPGAs. As the state-of-the-art FPGAs still lack the gate density necessary to the implementation of large neural networks of thousands of neurons, we use a stochastic process to implement efficiently the computation performed by a neuron. This paper describes and compares the characteristics of two architectures designed to implement feed-forward fully connected artificial neural networks: the first FPGA prototype is based on traditional adders and multipliers of binary inputs while the second takes advantage of stochastic representation of the inputs. The paper compares both prototypes using the time × area classic factor.

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Acknowledgment

We are grateful to the reviewers and the editor that contributed to the great improvement of the original version of this paper with their valuable comments and suggestions. We also are thankful to FAPERJ (Fundação de Amparo à Pesquisa do Estado do Rio de janeiro, http://www.faperj.br) and CNPq (Conselho Nacional de Desenvolvimento Científico e Tecnológico, http://www.cnpq.br) for their continuous financial support.

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Authors and Affiliations

  1. Department of Electronics Engineering and Telecommunications, Faculty of Engineering, State University of Rio de Janeiro, Rio de Janeiro, Brazil

    Nadia Nedjah

  2. Department of Systems Engineering and Computation, Faculty of Engineering, State University of Rio de Janeiro, Rio de Janeiro, Brazil

    Luiza de Macedo Mourelle

Authors
  1. Nadia Nedjah
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  2. Luiza de Macedo Mourelle
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Correspondence to Nadia Nedjah.

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Nedjah, N., de Macedo Mourelle, L. Reconfigurable hardware for neural networks: binary versus stochastic. Neural Comput & Applic 16, 249–255 (2007). https://doi.org/10.1007/s00521-007-0086-x

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  • DOI: https://doi.org/10.1007/s00521-007-0086-x

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