Hardware design of a neural processing unit for bio-inspired computing

Unsupervised artificial neural networks are now considered as a likely alternative to classical computing models in many application domains. For example, recent neural models defined by neuro-scientists exhibit interesting properties for an execution in embedded and autonomous systems: distributed computing, unsupervised learning, self-adaptation, self-organisation, tolerance. But these properties only emerge from large scale and fully connected neural maps that result in intensive computation coupled with high synaptic communications. We are interested in deploying these powerful models in the embedded context of an autonomous bio-inspired robot learning its environment in realtime. So we study in this paper in what extent these complex models can be simplified and deployed in hardware accelerators compatible with an embedded integration. Thus we propose a Neural Processing Unit designed as a programmable accelerator implementing recent equations close to self-organizing maps and neural fields. The proposed architecture is validated on FPGA devices and compared to state of the art solutions. The trade-off proposed by this dedicated but programmable neural processing unit allows to achieve significant improvements and makes our architecture adapted to many embedded systems.