Pruning Self-Organizing Maps for Cellular Hardware Architectures

Description

Self-organization is a bio-inspired feature that has been poorly developed when it comes to talking about hardware architectures. Cellular computing approaches have tackled it without considering input data. This paper introduces the SOMA architecture, which proposes an approach for self-organizing machine architectures. In order to achieve the desirable features for such machine, we propose PCSOM, a bio-inspired approach for self-organizing cellular hardware architectures in function of input data. PCSOM is a vector quantization algorithm defined as a network of neurons interconnected through synapses. Synapse pruning makes it possible to organize the cellular system architecture (i.e. topology and configuration of computing elements) in function of the content of input data. We present performance results of the algorithm and we discuss the benefits of PCSOM compared to other existing algorithms.

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