Abstract
A very large number of works concerning the area of Artificial Neural Networks deal with implementation of these models as software but also hardware solutions. However, hardware implementations of these models and issued solutions have essentially concerned the execution speed aspects. Today, a new question becomes unavoidable: taking into account the actual computers operation speeds (exceeding several Giga-operations per second), the specific hardware implementation of Artificial Neural Networks is it still an pertinent subject? This paper deals with two main goals. The first one is related to ANN's hardware implementation showing how theoretical bases of ANNs could lead to electronic implementation of these intelligent techniques. The second aim of the paper is to discuss the above formulated question through learning plasticity and robustness of ANN hardware implementations.
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Madani, K. (2005). From Integrated Circuits Technology to Silicon Grey Matter: Hardware Implementation of Artificial Neural Networks. In: Saeed, K., Pejaś, J. (eds) Information Processing and Security Systems. Springer, Boston, MA. https://doi.org/10.1007/0-387-26325-X_31
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DOI: https://doi.org/10.1007/0-387-26325-X_31
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