Hybrid Pipeline Structure for Self-Organizing Learning Array

Authors:
Janusz A. Starzyk;Mingwei Ding;Yinyin Liu
Affiliations:
School of Electrical Engineering & Computer Science, Ohio University, Athens, OH 45701,;School of Electrical Engineering & Computer Science, Ohio University, Athens, OH 45701,;School of Electrical Engineering & Computer Science, Ohio University, Athens, OH 45701,
Venue:
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Part II--Advances in Neural Networks
Year:
2007

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Abstract

In recent years, many efforts have been put in applying the concept of reconfigurable computing to neural networks. In our previous pursuits, an innovative self-organizing learning array (SOLAR) was developed. However, traditional multiplexer method to achieve reconfigurable connection has its limit for larger networks. In this paper, we propose a novel pipeline structure, which offers flexible, possibly large number of dynamically configurable connections and which utilizes each node's computing ability. The hardware resources demand of the proposed structure is a linear function of the network size, which is especially useful for building a large network that can handle complicated real-world applications.