Abstract
The aim of the research reported in this paper was to assess the scalability of a binary correlation Matrix Memory (CMM) based on the PRESENCE (PaRallEl StructurEd Neural Computing Engine) architecture. A single PRESENCE card has a finite memory capacity, and this paper describes how multiple PCI-based PRESENCE cards are utilised in order to scale up memory capacity and performance. A Beowulf class cluster, called Cortex-1, provides the scalable I/O capacity needed for multiple cards, and techniques for mapping applications onto the system are described. The main aims of the work are to prove the scalability of the AURA architecture, and to demonstrate the capabilities of the architecture for commercial pattern matching problems.
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© 2001 Springer-Verlag Berlin Heidelberg
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Weeks, M., Austin, J., Moulds, A., Turner, A., Ulanowski, Z., Young, J. (2001). Mapping Correlation Matrix Memory Applications onto a Beowulf Cluster. In: Dorffner, G., Bischof, H., Hornik, K. (eds) Artificial Neural Networks — ICANN 2001. ICANN 2001. Lecture Notes in Computer Science, vol 2130. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44668-0_22
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DOI: https://doi.org/10.1007/3-540-44668-0_22
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