Neuromorphic hardware as a self-organizing computing system

Lyes Khacef 1 Bernard Girau 2 Nicolas Rougier 3 Andres Upegui 4 Benoıt Miramond 1
2 BISCUIT - Bio-Inspired, Situated and Cellular Unconventional Information Technologies
LORIA - AIS - Department of Complex Systems, Artificial Intelligence & Robotics
3 Mnemosyne - Mnemonic Synergy
LaBRI - Laboratoire Bordelais de Recherche en Informatique, Inria Bordeaux - Sud-Ouest, IMN - Institut des Maladies Neurodégénératives [Bordeaux]
Abstract : This paper presents the self-organized neuromorphic architecture named SOMA. The objective is to study neural-based self-organization in computing systems and to prove the feasibility of a self-organizing hardware structure. Considering that these properties emerge from large scale and fully connected neural maps, we will focus on the definition of a self-organizing hardware architecture based on digital spiking neurons that offer hardware efficiency. From a biological point of view, this corresponds to a combination of the so-called synaptic and structural plasticities. We intend to define computational models able to simultaneously self-organize at both computation and communication levels, and we want these models to be hardware-compliant, fault tolerant and scalable by means of a neuro-cellular structure.
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Lyes Khacef, Bernard Girau, Nicolas Rougier, Andres Upegui, Benoıt Miramond. Neuromorphic hardware as a self-organizing computing system. WCCI 2018 - IEEE World Congress on Computational Intelligence, Workshop NHPU : Neuromorphic Hardware In Practice and Use, Jul 2018, Rio de Janeiro, Brazil. pp.1-4. ⟨hal-01790776⟩

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