Neuromorphic hardware as a self-organizing computing system

International audienceThis 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

Bio-inspired self-organizing cellular systems

Bio-inspiration borrows three properties characteristic of living organisms: multicellular architecture, cellular division, and cellular differentiation. Implemented in silicon according to these properties, our self-organizing systems are able to grow, to self-replicate, and to self-repair. The growth and branching processes, performed by the so-called Tom Thumb algorithm, lead thus to the configuration and cloning mechanisms of the systems. The repair processes allow its cicatrization and regeneration mechanisms. The cellular design and hardware implementation of these mechanisms constitute the core of this paper. (C) 2008 Elsevier Ireland Ltd. All rights reserved