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Research on Load Distribution Techniques at the Software Level in...
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Research on Load Distribution Techniques at the Software Level in Mobile Embedded Systems

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

Deadline for manuscript submissions: closed (30 September 2022) | Viewed by 14485

Special Issue Editors

Prof. Dr. Young-Jin Kim

E-Mail Website
Guest Editor
ECE, Ajou University, Suwon-si 16499, Republic of Korea
Interests: embedded systems and software; low-power technology; embedded deep learning
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Hoeseok Yang

E-Mail Website
Guest Editor
Department of Electrical and Computer Engineering, Ajou University, Suwon 16499, Korea
Interests: HW/SW codesign; reliability- and temperature-aware optimization/analysis of multiprocessor system-on-chip (MPSoC); embedded systems design with non-volatile memories; deep learning for embedded systems

Special Issue Information

Dear Colleagues,

In recent years, mobile embedded devices such as smartphones and tablets have grown in popularity among end-users for various human–computer interactions. Furthermore, a lot of IoT devices gather and process data in connection with cloud servers for smart factories, smart cities, and so on. The problem is that such devices still have insufficient computing power in comparison to PCs and servers. Thus, efficient load distribution techniques are urgently required. For example, the load of accelerating deep learning inference can be distributed among multiple mobile devices and a cloud server. To solve this problem, many studies have been conducted at the component and architecture level of CPU, GPU, memory, and SSD, and at the software level of operating system, middleware, compiler, library, and application.

In this Special Issue, original research articles as well as review articles that deal with system/application software and design architectures for new embedded technologies are invited. Potential topics include but are not limited to: 

  • Advanced load distribution embedded technologies;
  • Advanced high-performance/low-power embedded technologies;
  • Advanced hardware/software/architecture for future embedded technologies;
  • Advanced mobile computing systems and technologies;
  • Deep learning, AR/VR, image processing acceleration techniques for embedded systems;
  • Novel power modeling for mobile embedded systems.

Prof. Dr. Young-Jin Kim
Prof. Dr. Hoeseok Yang
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. 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

  • embedded systems and software
  • mobile computing
  • display systems and image processing
  • deep learning
  • low-power technology
  • high-performance computing

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Published Papers (5 papers)

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Research

19 pages, 2414 KiB  
Article
Phase-Based Low Power Management Combining CPU and GPU for Android Smartphones
by Seung-Ryeol Ohk, YongSin Kim and Young-Jin Kim
Electronics 2022, 11(16), 2480; https://doi.org/10.3390/electronics11162480 - 9 Aug 2022
Cited by 2 | Viewed by 2071
Abstract
Smartphones have limited battery capacity, so efficient power management is required for high-performance applications and to increase usage time. In recent years, efficient power management of smartphones has become very important as the demand for power use of smartphones has grown due to [...] Read more.
Smartphones have limited battery capacity, so efficient power management is required for high-performance applications and to increase usage time. In recent years, efficient power management of smartphones has become very important as the demand for power use of smartphones has grown due to deep learning, games, virtual reality, and augmented reality applications. Existing low-power techniques of smartphones focus only on lowering power consumption without considering actual power consumption based on utilization of the central processing unit (CPU) and graphics processing unit (GPU), which are major components of smartphones. In addition, they do not take into consideration the strict use of resources within the component and what instructions are being processed to operate them. In this paper, we propose a low-power technique that manages power by calculating the actual power consumption of smartphones at execution time and classifying the detailed resource operating states of CPUs and GPUs. The proposed technique was implemented by linking the kernel and native app on a Galaxy S7 smartphone equipped with Android. In experiments with 15 workloads, the proposed technique achieves an energy reduction of 18.11% compared to the low-power technique of the interactive governor built into the Galaxy S7 with a small FPS reduction of 3.12%. Full article
(This article belongs to the Special Issue Research on Load Distribution Techniques at the Software Level in Mobile Embedded Systems)
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12 pages, 679 KiB  
Article
Performance Improvement of Image-Reconstruction-Based Defense against Adversarial Attack
by Jungeun Lee and Hoeseok Yang
Electronics 2022, 11(15), 2372; https://doi.org/10.3390/electronics11152372 - 28 Jul 2022
Viewed by 1536
Abstract
Deep Neural Networks (DNNs) used for image classification are vulnerable to adversarial examples, which are images that are intentionally generated to predict an incorrect output for a deep learning model. Various defense methods have been proposed to defend against such adversarial attacks, among [...] Read more.
Deep Neural Networks (DNNs) used for image classification are vulnerable to adversarial examples, which are images that are intentionally generated to predict an incorrect output for a deep learning model. Various defense methods have been proposed to defend against such adversarial attacks, among which, image-reconstruction-based defense methods, such as DIPDefend, are known to be effective in getting rid of the adversarial perturbations injected in the image. However, this image-reconstruction-based defense approach suffers from a long execution time due to its iterative and time-consuming image reconstruction. The trade-off between the execution time and the robustness/accuracy of the defense method should be carefully explored, which is the main focus of this paper. In this work, we aim to improve the execution time of the existing state-of-the-art image-reconstruction-based defense method, DIPDefend, against the Fast Gradient Sign Method (FGSM). In doing so, we propose to take the input-specific properties into consideration when deciding the stopping point of the image reconstruction of DIPDefend. For that, we first applied a low-pass filter to the input image with various kernel sizes to make a prediction of the true label. Then, based on that, the parameters of the image reconstruction procedure were adaptively chosen. Experiments with 500 randomly chosen ImageNet validation set images show that we can obtain an approximately 40% improvement in execution time while keeping the accuracy drop as small as 0.4–3.9%. Full article
(This article belongs to the Special Issue Research on Load Distribution Techniques at the Software Level in Mobile Embedded Systems)
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19 pages, 43958 KiB  
Article
Dual Image Deblurring Using Deep Image Prior
by Chang Jong Shin, Tae Bok Lee and Yong Seok Heo
Electronics 2021, 10(17), 2045; https://doi.org/10.3390/electronics10172045 - 24 Aug 2021
Cited by 7 | Viewed by 3288
Abstract
Blind image deblurring, one of the main problems in image restoration, is a challenging, ill-posed problem. Hence, it is important to design a prior to solve it. Recently, deep image prior (DIP) has shown that convolutional neural networks (CNNs) can be a powerful [...] Read more.
Blind image deblurring, one of the main problems in image restoration, is a challenging, ill-posed problem. Hence, it is important to design a prior to solve it. Recently, deep image prior (DIP) has shown that convolutional neural networks (CNNs) can be a powerful prior for a single natural image. Previous DIP-based deblurring methods exploited CNNs as a prior when solving the blind deburring problem and performed remarkably well. However, these methods do not completely utilize the given multiple blurry images, and have limitations of performance for severely blurred images. This is because their architectures are strictly designed to utilize a single image. In this paper, we propose a method called DualDeblur, which uses dual blurry images to generate a single sharp image. DualDeblur jointly utilizes the complementary information of multiple blurry images to capture image statistics for a single sharp image. Additionally, we propose an adaptive L2_SSIM loss that enhances both pixel accuracy and structural properties. Extensive experiments show the superior performance of our method to previous methods in both qualitative and quantitative evaluations. Full article
(This article belongs to the Special Issue Research on Load Distribution Techniques at the Software Level in Mobile Embedded Systems)
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15 pages, 41453 KiB  
Article
Deep Image Prior for Super Resolution of Noisy Image
by Sujy Han, Tae Bok Lee and Yong Seok Heo
Electronics 2021, 10(16), 2014; https://doi.org/10.3390/electronics10162014 - 20 Aug 2021
Cited by 5 | Viewed by 4931
Abstract
Single image super-resolution task aims to reconstruct a high-resolution image from a low-resolution image. Recently, it has been shown that by using deep image prior (DIP), a single neural network is sufficient to capture low-level image statistics using only a single image without [...] Read more.
Single image super-resolution task aims to reconstruct a high-resolution image from a low-resolution image. Recently, it has been shown that by using deep image prior (DIP), a single neural network is sufficient to capture low-level image statistics using only a single image without data-driven training such that it can be used for various image restoration problems. However, super-resolution tasks are difficult to perform with DIP when the target image is noisy. The super-resolved image becomes noisy because the reconstruction loss of DIP does not consider the noise in the target image. Furthermore, when the target image contains noise, the optimization process of DIP becomes unstable and sensitive to noise. In this paper, we propose a noise-robust and stable framework based on DIP. To this end, we propose a noise-estimation method using the generative adversarial network (GAN) and self-supervision loss (SSL). We show that a generator of DIP can learn the distribution of noise in the target image with the proposed framework. Moreover, we argue that the optimization process of DIP is stabilized when the proposed self-supervision loss is incorporated. The experiments show that the proposed method quantitatively and qualitatively outperforms existing single image super-resolution methods for noisy images. Full article
(This article belongs to the Special Issue Research on Load Distribution Techniques at the Software Level in Mobile Embedded Systems)
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17 pages, 1552 KiB  
Article
Communication Failure Resilient Distributed Neural Network for Edge Devices
by Jonghun Jeong, Jong Sung Park and Hoeseok Yang
Electronics 2021, 10(14), 1614; https://doi.org/10.3390/electronics10141614 - 6 Jul 2021
Viewed by 1756
Abstract
Recently, the necessity to run high-performance neural networks (NN) is increasing even in resource-constrained embedded systems such as wearable devices. However, due to the high computational and memory requirements of the NN applications, it is typically infeasible to execute them on a single [...] Read more.
Recently, the necessity to run high-performance neural networks (NN) is increasing even in resource-constrained embedded systems such as wearable devices. However, due to the high computational and memory requirements of the NN applications, it is typically infeasible to execute them on a single device. Instead, it has been proposed to run a single NN application cooperatively on top of multiple devices, a so-called distributed neural network. In the distributed neural network, workloads of a single big NN application are distributed over multiple tiny devices. While the computation overhead could effectively be alleviated by this approach, the existing distributed NN techniques, such as MoDNN, still suffer from large traffics between the devices and vulnerability to communication failures. In order to get rid of such big communication overheads, a knowledge distillation based distributed NN, called Network of Neural Networks (NoNN), was proposed, which partitions the filters in the final convolutional layer of the original NN into multiple independent subsets and derives smaller NNs out of each subset. However, NoNN also has limitations in that the partitioning result may be unbalanced and it considerably compromises the correlation between filters in the original NN, which may result in an unacceptable accuracy degradation in case of communication failure. In this paper, in order to overcome these issues, we propose to enhance the partitioning strategy of NoNN in two aspects. First, we enhance the redundancy of the filters that are used to derive multiple smaller NNs by means of averaging to increase the immunity of the distributed NN to communication failure. Second, we propose a novel partitioning technique, modified from Eigenvector-based partitioning, to preserve the correlation between filters as much as possible while keeping the consistent number of filters distributed to each device. Throughout extensive experiments with the CIFAR-100 (Canadian Institute For Advanced Research-100) dataset, it has been observed that the proposed approach maintains high inference accuracy (over 70%, 1.53× improvement over the state-of-the-art approach), on average, even when a half of eight devices in a distributed NN fail to deliver their partial inference results. Full article
(This article belongs to the Special Issue Research on Load Distribution Techniques at the Software Level in Mobile Embedded Systems)
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