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Sensors 2018, 18(6), 1877; https://doi.org/10.3390/s18061877

FPGA-Based High-Performance Embedded Systems for Adaptive Edge Computing in Cyber-Physical Systems: The ARTICo3 Framework

1
Centro de Electrónica Industrial, Universidad Politécnica de Madrid, José Gutiérrez Abascal 2, 28006 Madrid, Spain
2
United Technologies Research Centre (UTRC), Penrose Wharf, Cork T23 XN53, Ireland
*
Author to whom correspondence should be addressed.
Received: 6 April 2018 / Revised: 5 June 2018 / Accepted: 5 June 2018 / Published: 8 June 2018
(This article belongs to the Special Issue Green Communications and Networking for IoT)
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Abstract

Cyber-Physical Systems are experiencing a paradigm shift in which processing has been relocated to the distributed sensing layer and is no longer performed in a centralized manner. This approach, usually referred to as Edge Computing, demands the use of hardware platforms that are able to manage the steadily increasing requirements in computing performance, while keeping energy efficiency and the adaptability imposed by the interaction with the physical world. In this context, SRAM-based FPGAs and their inherent run-time reconfigurability, when coupled with smart power management strategies, are a suitable solution. However, they usually fail in user accessibility and ease of development. In this paper, an integrated framework to develop FPGA-based high-performance embedded systems for Edge Computing in Cyber-Physical Systems is presented. This framework provides a hardware-based processing architecture, an automated toolchain, and a runtime to transparently generate and manage reconfigurable systems from high-level system descriptions without additional user intervention. Moreover, it provides users with support for dynamically adapting the available computing resources to switch the working point of the architecture in a solution space defined by computing performance, energy consumption and fault tolerance. Results show that it is indeed possible to explore this solution space at run time and prove that the proposed framework is a competitive alternative to software-based edge computing platforms, being able to provide not only faster solutions, but also higher energy efficiency for computing-intensive algorithms with significant levels of data-level parallelism. View Full-Text
Keywords: edge computing; Cyber-Physical Systems; FPGAs; Dynamic and Partial Reconfiguration; energy efficiency; fault tolerance edge computing; Cyber-Physical Systems; FPGAs; Dynamic and Partial Reconfiguration; energy efficiency; fault tolerance
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Rodríguez, A.; Valverde, J.; Portilla, J.; Otero, A.; Riesgo, T.; de la Torre, E. FPGA-Based High-Performance Embedded Systems for Adaptive Edge Computing in Cyber-Physical Systems: The ARTICo3 Framework. Sensors 2018, 18, 1877.

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