Neuromorphic hardware as a self-organizing computing system

Lyes Khacef (1) , Bernard Girau (2) , Nicolas P. Rougier (3) , Andres Upegui (4) , Benoit Miramond (1)

1 LEAT - Laboratoire d'Electronique, Antennes et Télécommunications
2 BISCUIT - Bio-Inspired, Situated and Cellular Unconventional Information Technologies
3 Mnemosyne - Mnemonic Synergy
4 University of Applied Sciences of Western Switzerland

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.

Domaines: Réseau de neurones [cs.NE]; Architectures Matérielles [cs.AR]; Bio-informatique [q-bio.QM]

Lyes Khacef, Bernard Girau, Nicolas P. Rougier, Andres Upegui, Benoit 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⟩