Hardware-Optimized Reservoir Computing System for Edge Intelligence Applications
| Autor: | Vincent Canals, Alejandro Morán, Saeid Safavi, Christian F. Frasser, Josep L. Rosselló, Fabio Galan-Prado, Dhinakar Radhakrishnan |
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| Rok vydání: | 2021 |
| Předmět: |
Computer science
business.industry Cognitive Neuroscience Circuit design Deep learning Reservoir computing 02 engineering and technology Convolutional neural network Computer Science Applications 03 medical and health sciences 0302 clinical medicine Computer engineering Proof of concept 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Computer Vision and Pattern Recognition Artificial intelligence Enhanced Data Rates for GSM Evolution business Field-programmable gate array 030217 neurology & neurosurgery Efficient energy use |
| Zdroj: | Cognitive Computation. |
| ISSN: | 1866-9964 1866-9956 |
| DOI: | 10.1007/s12559-020-09798-2 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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