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.
Locate the Document
DOI
10.1109/ICRC.2019.8914716
Persistent Identifier
https://archives.pdx.edu/ds/psu/34794
Publisher
IEEE
Citation Details
Tran, S. J. D., & Teuscher, C. (2019). Hierarchical Memcapacitive Reservoir Computing Architecture. Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/icrc.2019.8914716