Energy-Efficient Embedded Computing

A special issue of Journal of Low Power Electronics and Applications (ISSN 2079-9268).

Deadline for manuscript submissions: closed (31 March 2021) | Viewed by 4381

Special Issue Editors


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Guest Editor
Department of Informatics Engineering, Faculty of Engineering, University of Porto, 4200-465 Porto, Portugal
Interests: reconfigurable computing; Field-Programmable Custom Computing Machines (FCCMs); automation of software and hardware engineering tasks; high-performance embedded computing; Domain-Specific Languages (DSLs), Tools and Compilers

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Guest Editor
Computer Engineering Laboratory, Faculty of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology, 2628 CD Delft, The Netherlands
Interests: reconfigurable computing; embedded systems; hardware/software co-design; high-performance computing; distributed collaborative computing; network processing

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Guest Editor
Computer Engineering, Brandenburg University of Technology Cottbus-Senftenberg, Universitätsplatz 1, 01968 Senftenberg, Germany
Interests: reconfigurable computing; system on chip; embedded systems
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Special Issue Information

Embedded computing permeates our daily life in different forms, from electronic devices at our homes to mobile devices such as smartphones. Most computing devices in embedded systems impose strict constraints that require special development and design efforts. Reducing power dissipation and/or energy consumption is in many cases needed to obey those constraints and contribute to the success of many embedded systems. This Special Issue will be devoted to recent research achievements regarding energy-efficient software and hardware techniques for embedded computing. Specifically, articles about the following topics, all addressing embedded computing, are welcome:

  • Software-based optimizations for saving energy consumption;
  • Hardware-based optimizations for saving energy consumption;
  • Trade-offs between QoS/QoE and energy consumption;
  • Optimizing for power dissipation and energy consumption;
  • Runtime autotuning techniques for energy efficiency;
  • Runtime adaptability for energy efficiency;
  • Impact of data structures and types on energy efficiency;
  • Educational approaches regarding energy efficiency;
  • Best software/hardware practices for energy efficiency;
  • AI for energy-efficient software/hardware systems;
  • Energy-efficient computing using emerging non-volatile memory technologies;
  • Energy-efficient in-memory or near-memory computing.

Prof. Dr. João M. P. Cardoso
Dr. Stephan Wong
Prof. Dr. Michael Hüebner
Guest Editors

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Published Papers (1 paper)

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Research

34 pages, 1275 KiB  
Article
Statically Analyzing the Energy Efficiency of Software Product Lines
by Marco Couto, João Paulo Fernandes and João Saraiva
J. Low Power Electron. Appl. 2021, 11(1), 13; https://doi.org/10.3390/jlpea11010013 - 23 Mar 2021
Cited by 1 | Viewed by 3362
Abstract
Optimizing software to become (more) energy efficient is an important concern for the software industry. Although several techniques have been proposed to measure energy consumption within software engineering, little work has specifically addressed Software Product Lines (SPLs). SPLs are a widely used software [...] Read more.
Optimizing software to become (more) energy efficient is an important concern for the software industry. Although several techniques have been proposed to measure energy consumption within software engineering, little work has specifically addressed Software Product Lines (SPLs). SPLs are a widely used software development approach, where the core concept is to study the systematic development of products that can be deployed in a variable way, e.g., to include different features for different clients. The traditional approach for measuring energy consumption in SPLs is to generate and individually measure all products, which, given their large number, is impractical. We present a technique, implemented in a tool, to statically estimate the worst-case energy consumption for SPLs. The goal is to reason about energy consumption in all products of a SPL, without having to individually analyze each product. Our technique combines static analysis and worst-case prediction with energy consumption analysis, in order to analyze products in a feature-sensitive manner: a feature that is used in several products is analyzed only once, while the energy consumption is estimated once per product. This paper describes not only our previous work on worst-case prediction, for comprehensibility, but also a significant extension of such work. This extension has been realized in two different axis: firstly, we incorporated in our methodology a simulated annealing algorithm to improve our worst-case energy consumption estimation. Secondly, we evaluated our new approach in four real-world SPLs, containing a total of 99 software products. Our new results show that our technique is able to estimate the worst-case energy consumption with a mean error percentage of 17.3% and standard deviation of 11.2%. Full article
(This article belongs to the Special Issue Energy-Efficient Embedded Computing)
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