In this paper, we propose reconfigurable, low-cost and readily available hardware architecture for an artificial neuron. This is used to build a feed-forward artificial neural network. For this purpose, we use field-programmable gate arrays i.e. FPGAs. However, 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 the computation performed by a neuron. The multiplication an addition of stochastic values is simply implemented by an ensemble of XNOR and AND gates respectively. © Springer-Verlag Berlin Heidelberg 2003.
CITATION STYLE
Nedjah, N., & De Macedo Mourelle, L. (2003). Reconfigurable hardware architecture for compact and efficient stochastic neuron. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2687, 17–24. https://doi.org/10.1007/3-540-44869-1_3
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