A distributed cellular approach of large scale SOM models for hardware implementation
Résumé
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 lateral
synaptic 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
Mots clés
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