Special Issue "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 3448

Special Issue Editors

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
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
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

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Journal of Low Power Electronics and Applications is an international peer-reviewed open access quarterly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (1 paper)

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34 pages, 1275 KiB  
Statically Analyzing the Energy Efficiency of Software Product Lines
J. Low Power Electron. Appl. 2021, 11(1), 13; https://doi.org/10.3390/jlpea11010013 - 23 Mar 2021
Cited by 1 | Viewed by 2644
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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