Online Feature Learning from a non-i.i.d. Stream in a Neuromorphic System with Synaptic Competition

Abstract

Neuromorphic computing takes inspiration from how the brain works to design power- and area-efficient hardware architectures for learning systems. Recently, unsupervised feature learning neuromorphic architectures have been presented, including a concept of synaptic competition that promotes the engagement of the synapses in the learning beyond weight storage. However, it is common to train these neuromorphic systems following the classic machine learning assumption of i.i.d. Dataset sampling, which may not hold for real world inputs. In this paper, we propose a more realistic dataset sampling technique and apply it for online learning in a neuromorphic system using phase-change memristors as synapses and implementing synaptic competition. Furthermore, we propose a novel formulation of synaptic competition that captures orthogonal features, alternatively to independent components. We experimentally demonstrate the operation of the system for a non-i.i.d. Stream and compare the performance to the models of lateral inhibition and dendritic inhibition. The obtained results demonstrate online feature learning capabilities of the proposed system and robustness to non-i.i.d. Inputs.