Abstract
This paper describes nanocircuits that draw on negative differential resistance and are capable of implementing complex threshold functions in a single gate structure. Due to nanometer dimensions, high operational frequencies and low power consumption these devices can be used for efficient hardware realisation of artificial neural networks (ANNs). We present state of the art in development of such circuit and focus on Generalised Threshold Gates (GTGs) that are capable of implementing arbitrary Boolean functions in a single gate structure. Algorithm for implementing Boolean functions outputs circuits with predefined weights and thresholds. This enables to construct application specific ANNs and eliminates the requirement for network learning when this kind of gates are used.
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Nikodem, M. (2010). Hardware Implementation of Artificial Neural Networks for Arbitrary Boolean Functions with Generalised Threshold Gate Circuits. In: Sidorov, G., Hernández Aguirre, A., Reyes García, C.A. (eds) Advances in Soft Computing. MICAI 2010. Lecture Notes in Computer Science(), vol 6438. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16773-7_26
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DOI: https://doi.org/10.1007/978-3-642-16773-7_26
Publisher Name: Springer, Berlin, Heidelberg
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