Hardware-Optimized Reservoir Computing System for Edge Intelligence Applications

Edge artificial intelligence or edge intelligence is an ever-growing research area due to the current popularization of the Internet of Things. Unfortunately, incorporation of artificial intelligence (AI) in smart devices operating at the edge is a challenging task due to the power-hungry characteri...

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Bibliographic Details
Published in:Cognitive computation 2023-09, Vol.15 (5), p.1461-1469
Main Authors: Morán, Alejandro, Canals, Vincent, Galan-Prado, Fabio, Frasser, Christian F., Radhakrishnan, Dhinakar, Safavi, Saeid, Rosselló, Josep L.
Format: Article
Language:English
Subjects:
Accuracy
Artificial Intelligence
Artificial neural networks
Circuit design
Computation
Computation by Abstract Devices
Computational Biology/Bioinformatics
Computer Science
Connectivity
Deep learning
Design optimization
Energy consumption
Energy efficiency
Hardware
Internet of Things
Machine learning
Neural networks
Neurons
Power management
Trends in Reservoir Computing
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Summary:Edge artificial intelligence or edge intelligence is an ever-growing research area due to the current popularization of the Internet of Things. Unfortunately, incorporation of artificial intelligence (AI) in smart devices operating at the edge is a challenging task due to the power-hungry characteristics of deep learning implementations, such as convolutional neural networks (CNNs). As a feasible alternative, reservoir computing (RC) has attracted a lot of attention in the field of machine learning due to its promising performance in a wide range of applications. In this work, we propose a simple hardware-optimized circuit design of RC systems presenting high energy-efficiency capacities that fulfill the low power requirements of edge intelligence applications. As a proof of concept, we used the proposed design for the implementation of a low-power audio event detection (AED) application in FPGA. The measurements and simulation results obtained show that the proposed approach may provide significant accuracy with the advantage of presenting ultra-low-power characteristics (the energy efficiency estimated is below the microjoule per inference). These results make the proposed system optimal for edge intelligence applications in which energy efficiency and accuracy are the key issues.
ISSN:1866-9956
1866-9964
DOI:10.1007/s12559-020-09798-2