Title

Hierarchical Memcapacitive Reservoir Computing Architecture

Published In

2019 IEEE International Conference on Rebooting Computing (ICRC)

Document Type

Citation

Publication Date

11-1-2019

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.

DOI

10.1109/ICRC.2019.8914716

Persistent Identifier

https://archives.pdx.edu/ds/psu/34794

Publisher

IEEE

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