• A distributed cellular approac...

Treffer: A distributed cellular approach of large scale SOM models for hardware implementation

Title:
A distributed cellular approach of large scale SOM models for hardware implementation
Authors:
Rodriguez, Laurent, Khacef, Lyes, Miramond, Benoît
Contributors:
Laboratoire d'Electronique, Antennes et Télécommunications (LEAT), Université Nice Sophia Antipolis (1965 - 2019) (UNS)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UniCA), IEEE, NeuroMod, ANR-17-CE24-0036,SOMA,Auto-organisation dans les architectures matérielles neuromorphiques(2017)
Source:
IEEE Conference on Image Processing and Signals (IPAS). :250-255
Publisher Information:
CCSD, 2018.
Publication Year:
2018
Collection:
collection:UNICE
collection:CNRS
collection:UNIV-COTEDAZUR
collection:TEST-HALCNRS
collection:ANR
collection:LEAT
collection:UNIV-COTEDAZUR_COLLECTION_DEFAUT
collection:NEUROMOD
collection:TEST-NICE
Subject Terms:
particular cellular implementation, centralized models, computing complexity, cellular computing approaches, hardware neural processors, distributed cellular approach, neural models, large scale SOM models, hardware compliant algorithm, self-organizing machine architecture, Neurons, Computer architecture, Self-organizing feature maps, Hardware, Computational modeling, Synapses, Biological neural networks, self-organizing maps, cellular computing, neuromorphic architectures, hardware neural networks, cellular formulation, related neural models, SOMA hardware architecture, decentralized operations, self-organizing models, computational complexity, learning (artificial intelligence), neural net architecture, self-organising feature maps, hardware implementation, powerful bio-inspired feature, hardware computing architectures, neural foundations, brain self-organization, structural plasticity, continuous learning, massive lateral synaptic connections, [INFO.INFO-AR]Computer Science [cs], Hardware Architecture [cs.AR]
Subject Geographic:
Nice (sophia-antipolis), France
Original Identifier:
HAL: hal-01923134
Document Type:
Konferenz conferenceObject<br />Conference papers
Language:
English
Relation:
info:eu-repo/semantics/altIdentifier/doi/10.1109/IPAS.2018.8708904
DOI:
10.1109/IPAS.2018.8708904
Availability:
https://hal.science/hal-01923134
Accession Number:
edshal.hal.01923134v1
Database:
HAL

Weitere Informationen

Self-organization is a powerful bio-inspired feature that has been poorly investigated in the context of hardware computing architectures. The neural foundations of brain selforganization enable structural plasticity, continuous learning,and tolerance to lesions but at the cost of massive lateralsynaptic connections. A cellular formulation of the related neural models would be able to tackle this limitation by iterating the propagation of the information in the network. This paper introduces the SOMA hardware architecture, which relies on this principle to develop a Self-Organizing Machine Architecture. We show the benefits of our cellular implementation compared to the existing neural models. This contribution leads to a hardware compliant algorithm thanks to decentralized operations and communications. A generalization of the technique is proposed to implement different Kohonen-like self-organizing models. This particular cellular implementation reaches the same behavior as the centralized models but drastically reduces their computing complexity. We present the performance results and discuss the benefits of cellular computing approaches in the perspective of designing hardware neural processors for embedded applications