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Electronics
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Recent Advancements in Approximate Ubiquitous Computing
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Recent Advancements in Approximate Ubiquitous Computing

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: closed (28 February 2022) | Viewed by 5606

Special Issue Editor

Prof. Dr. Veljko Pejovic

E-Mail Website
Guest Editor
Faculty of Computer and Information Science, University of Ljubljana, 1000 Ljubljana, Slovenia
Interests: ubiquitous computing; approximate mobile computing; human–computer interaction; mobile sensing; technology and society

Special Issue Information

Dear Colleagues,

For decades Dennard scaling, a law describing the area-proportional growth of integrated circuit power use, has provided assurance that every subsequent generation of computing devices will pack more computing power in an even smaller package. However, recently it has become obvious that this observation does not hold true any longer and that future transistors will require more energy for operation and cooling. Due to their small size and limited battery power, ubiquitous computing devices are particularly threatened. At the same time, smartphones, wearables, IoT devices, and drones are becoming critical infrastructure and are expected to carry the burden of advanced computing applications of the future.

To ensure the further proliferation of ubiquitous computing in the light of these imminent physical limitations, we have to reconsider the amount of computation handled by these devices. Approximate computing, whereby the resulting quality is deliberately sacrificed in a controlled manner in order to reduce the amount of computation, has suddenly moved into the spotlight as a viable technique for reducing the resource appetite and ensuring that advanced algorithms of the future run on ubiquitous computing devices.

In this Special Issue, we are particularly interested in showcasing recent advancements in the area of approximate ubiquitous computing, as well as in presenting recent results analyzing the impacts that such techniques might have on future applications, on the sustainability of ubiquitous computing, and on the way ubiquitous computing solutions interact with human users. The topics of interest include, but are not limited to:

  • Approximate circuits for ubiquitous computing;
  • Approximate computing techniques for modern mobile and wearable devices;
  • Approximation-enabled deep learning for resource-constrained devices;
  • Hardware–software co-design for approximate mobile and IoT systems;
  • Context-aware approximation systems;
  • Human perception and approximate computing.

Prof. Dr. Veljko Pejovic
Guest Editor

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. Electronics is an international peer-reviewed open access semimonthly 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 2400 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.

Keywords

  • approximate computing
  • ubiquitous computing
  • pervasive computing
  • mobile computing
  • energy efficiency
  • edge computing
  • on-device deep learning
  • context awareness
  • mobile sensing

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Published Papers (2 papers)

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Research

20 pages, 3758 KiB  
Article
Approximate Computing Circuits for Embedded Tactile Data Processing
by Mario Osta, Ali Ibrahim and Maurizio Valle
Electronics 2022, 11(2), 190; https://doi.org/10.3390/electronics11020190 - 8 Jan 2022
Viewed by 2192
Abstract
In this paper, we demonstrate the feasibility and efficiency of approximate computing techniques (ACTs) in the embedded Support Vector Machine (SVM) tensorial kernel circuit implementation in tactile sensing systems. Improving the performance of the embedded SVM in terms of power, area, and delay [...] Read more.
In this paper, we demonstrate the feasibility and efficiency of approximate computing techniques (ACTs) in the embedded Support Vector Machine (SVM) tensorial kernel circuit implementation in tactile sensing systems. Improving the performance of the embedded SVM in terms of power, area, and delay can be achieved by implementing approximate multipliers in the SVD. Singular Value Decomposition (SVD) is the main computational bottleneck of the tensorial kernel approach; since digital multipliers are extensively used in SVD implementation, we aim to optimize the implementation of the multiplier circuit. We present the implementation of the approximate SVD circuit based on the Approximate Baugh-Wooley (Approx-BW) multiplier. The approximate SVD achieves an energy consumption reduction of up to 16% at the cost of a Mean Relative Error decrease (MRE) of less than 5%. We assess the impact of the approximate SVD on the accuracy of the classification; showing that approximate SVD increases the Error rate (Err) within a range of one to eight percent. Besides, we propose a hybrid evaluation test approach that consists of implementing three different approximate SVD circuits having different numbers of approximated Least Significant Bits (LSBs). The results show that energy consumption is reduced by more than five percent with the same accuracy loss. Full article
(This article belongs to the Special Issue Recent Advancements in Approximate Ubiquitous Computing)
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23 pages, 1536 KiB  
Article
Self-Adaptive Approximate Mobile Deep Learning
by Timotej Knez, Octavian Machidon and Veljko Pejović
Electronics 2021, 10(23), 2958; https://doi.org/10.3390/electronics10232958 - 28 Nov 2021
Cited by 5 | Viewed by 2466
Abstract
Edge intelligence is currently facing several important challenges hindering its performance, with the major drawback being meeting the high resource requirements of deep learning by the resource-constrained edge computing devices. The most recent adaptive neural network compression techniques demonstrated, in theory, the potential [...] Read more.
Edge intelligence is currently facing several important challenges hindering its performance, with the major drawback being meeting the high resource requirements of deep learning by the resource-constrained edge computing devices. The most recent adaptive neural network compression techniques demonstrated, in theory, the potential to facilitate the flexible deployment of deep learning models in real-world applications. However, their actual suitability and performance in ubiquitous or edge computing applications has not, to this date, been evaluated. In this context, our work aims to bridge the gap between the theoretical resource savings promised by such approaches and the requirements of a real-world mobile application by introducing algorithms that dynamically guide the compression rate of a neural network according to the continuously changing context in which the mobile computation is taking place. Through an in-depth trace-based investigation, we confirm the feasibility of our adaptation algorithms in offering a scalable trade-off between the inference accuracy and resource usage. We then implement our approach on real-world edge devices and, through a human activity recognition application, confirm that it offers efficient neural network compression adaptation in highly dynamic environments. The results of our experiment with 21 participants show that, compared to using static network compression, our approach uses 2.18× less energy with only a 1.5% drop in the average accuracy of the classification. Full article
(This article belongs to the Special Issue Recent Advancements in Approximate Ubiquitous Computing)
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