Abstract
Artificial neural networks are able to solve a great variety of different applications, e.g. classification or approximation tasks. To utilize their advantages in technical systems various hardware realizations do exist. In this work, the impact of shrinking device sizes on the activation function of neurons is investigated with respect to area demands, power consumption and the maximum resolution in their information processing. Furthermore, analog and digital implementations are compared in emerging silicon technologies beyond 100 nm feature size.
This work was supported by the Graduate College 776 - Automatic Configuration in Open Systems - funded by the German Research Foundation (DFG).
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Eickhoff, R., Kaulmann, T., Rückert, U. (2007). Impact of Shrinking Technologies on the Activation Function of Neurons. In: de Sá, J.M., Alexandre, L.A., Duch, W., Mandic, D. (eds) Artificial Neural Networks – ICANN 2007. ICANN 2007. Lecture Notes in Computer Science, vol 4668. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74690-4_51
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DOI: https://doi.org/10.1007/978-3-540-74690-4_51
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