Hierarchical Memcapacitive Reservoir Computing Architecture

Abstract

The quest for novel computing architectures is currently driven by (1) machine learning applications and (2) the need to reduce power consumption. To address both needs, we present a novel hierarchical reservoir computing architecture that relies on energy-efficient memcapacitive devices. Reservoir computing is a new brain-inspired machine learning architecture that typically relies on a monolithic, i.e., unstructured, network of devices. We use memcapacitive devices to perform the computations because they do not consume static power. Our results show that hierarchical memcapacitive reservoir computing device networks have a higher kernel quality, outperform monolithic reservoirs by 10%, and reduce the power consumption by a factor of 3.4× on our benchmark tasks. The proposed new architecture is relevant for building novel, adaptive, and power-efficient neuromorphic hardware with applications in embedded systems, the Internet-of-Things, and robotics.