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

Description

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

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