Abstract
The architecture of linearly expandable partial tree shape neurocomputer (PARNEU) is presented. The system is designed for efficient, general-purpose artificial neural network computations utilizing parallel processing. Linear expandability is due to modular architecture, which combines bus, ring and tree topologies. Mappings of algorithms are presented for Hopfield and perceptron networks, Sparse Distributed Memory, and Self-Organizing Map. Performance is discussed with figures of computational complexity.
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CITATION STYLE
Hämäläinen, T., Kolinummi, P., & Kaski, K. (1996). Linearly expandable partial tree shape architecture for parallel neurocomputer. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 1112 LNCS, pp. 365–370). Springer Verlag. https://doi.org/10.1007/3-540-61510-5_64
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