Information coding and hardware architecture of spiking neural networks

Nassim Abderrahmane (1) , Benoit Miramond (1)

1 LEAT - Laboratoire d'Electronique, Antennes et Télécommunications

Inspired from the brain, neuromorphic computing would be the right alternative to traditional Von-Neumann architecture computing that knows its end of growth as predicted by Moore’s law. In this paper, we explore bio-inspired neural networks as an AI-accelerator for embedded systems. To do so, we first map neural networks from formal to spiking domain, then choose the information coding method resulting in better performances. Afterwards, we present the design of two different hardware architectures: time-multiplexed and fully-parallel. Finally, we compare their performances and their hardware cost to select at the end the adequate architecture and conclude about spike-based neural networks as a potential solution for embedded artificial intelligence applications.

Mots clés: Encoding; Hardware; Neurons; Artificial Neural Networks; Embedded Systems; Hardware Accelerator; Power Consumption; Bio-inspired Computing; Neuromorphic Computing; Spiking Neural Networks; Power demand; Computer architecture; Biological neural networks

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

Nassim Abderrahmane, Benoit Miramond. Information coding and hardware architecture of spiking neural networks. Euromicro DSD/SEAA 2019, Aug 2019, Kallithea,Chalkidiki, Greece. pp.291-298, ⟨10.1109/DSD.2019.00050⟩. ⟨hal-02179656⟩