Pruning Self-Organizing Maps for Cellular Hardware Architectures

Abstract : 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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Contributor : Sophie Gaffé-Clément <>
Submitted on : Thursday, August 30, 2018 - 9:13:27 AM
Last modification on : Thursday, February 21, 2019 - 3:58:53 PM


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Andres Upegui, Bernard Girau, Nicolas P. Rougier, Fabien Vannel, Benoıt Miramond. Pruning Self-Organizing Maps for Cellular Hardware Architectures. AHS 2018 - 12th NASA/ESA Conference on Adaptive Hardware and Systems, Aug 2018, Edinburgh, United Kingdom. pp.272-279, ⟨10.1109/AHS.2018.8541465⟩. ⟨hal-01826263⟩



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