Real-Time Clustering of Datasets with Hardware Embedded Neuromorphic Neural Networks

Abstract

Neuromorphic artificial neural networks attempt to understand the essential computations that take place in the dense networks of interconnected neurons making up the central nervous systems in living creatures. This paper demonstrates that artificial spiking neural networks, – built to resemble the biological model– encoding information in the timing of single spikes are capable of computing and learning clusters from realistic data. It shows how a spiking neural network based on spike-time coding can successfully perform unsupervised and supervised clustering on real-world data. A temporal encoding of continuously valued data is developed. These models are validated through software simulation and then used to develop suitable hardware implementations on FPGA circuits. Fully parallel implementations are investigated and compared with solutions that make use of embedded soft-core microcontrollers to implement some of the most resource-consuming components of the artificial neural network. Details of the implementation are given, with test bench description. Measurement results are presented and compared to related findings in the specific literature.