Abstract
On chip resource distribution is a problem that, due to its complexity, is susceptible to be solved by using artificial intelligence optimization procedures. In this paper, a Hopfield recurrent neural network and a Boltzmann machine are proposed for searching good solutions.
The main challenge of this approach is proposing an energy function to be minimized so it mixes all the problem-related restrictions.
Experimental data shows that we can get good enough solutions in a reasonable time using Hopfield nets or close to the global minimum solutions using Boltzmann machines.
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Sánchez Jurado, F.J., Santos Pen̈as, M. (2007). Hopfield Neural Network and Boltzmann Machine Applied to Hardware Resource Distribution on Chips. In: Mira, J., Álvarez, J.R. (eds) Bio-inspired Modeling of Cognitive Tasks. IWINAC 2007. Lecture Notes in Computer Science, vol 4527. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73053-8_39
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DOI: https://doi.org/10.1007/978-3-540-73053-8_39
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-73052-1
Online ISBN: 978-3-540-73053-8
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