Special Issue "Energy-Efficient and Reliable Information Processing: Computing and Storage"

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

Deadline for manuscript submissions: closed (31 March 2019).

Special Issue Editor

Dr. Yongjune Kim
Website
Guest Editor
Western Digital Research, Milpitas, CA, USA
Interests: Coding and information theory for nanoscale devices, resource-constrained machine learning, energy-efficient computing and storage

Special Issue Information

Dear Colleagues,

Recently, artificial intelligence (AI) systems have begun to approach and exceed human performance in many intelligent tasks: AlexNet [1] and ResNet [2] achieving human-level accuracy in recognition tasks; AlphaGo [3] beating human champions in Go. These acclaimed successes of AI are mainly based on computations using massive amounts of data. Two pillars of modern AI systems are computation and data, and this information processing has to take place efficiently and reliably. The main aim of this Special Issue is to seek high-quality submissions that address energy-efficient and reliable computing and data storage systems. The topics of interest include, but are not limited to:

  • Fundamental limits of information processing: Computing and storage
  • Stochastic computing, approximate computing, Shannon-inspired computing, fault-tolerant computing, error-resilient computing, and neuromorphic computing
  • In-memory computing and near-data computing
  • Distributed computing and storage systems
  • Channel coding and signal processing for data storage

[1] A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” in Proc. Annu. Conf. Neural Inf. Process. Syst. (NIPS), Dec. 2012, pp. 1097–1105.
[2] K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognition (CVPR), Jun. 2016, pp. 770–778.
[3] D. Silver et al., “Mastering the game of Go with deep neural networks and tree search,” Nature, vol. 529, no. 7587, pp. 484–489, Jan. 2016.

Dr. Yongjune Kim
Guest Editor

Manuscript Submission Information

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Published Papers (6 papers)

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Editorial

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Open AccessEditorial
Editorial of Energy-Efficient and Reliable Information Processing: Computing and Storage
Electronics 2019, 8(9), 914; https://doi.org/10.3390/electronics8090914 - 21 Aug 2019
Cited by 1
Abstract
Recently, artificial intelligence (AI) systems have begun to approach and exceed human performance in many intelligent tasks: AlexNet [...] Full article

Research

Jump to: Editorial

Open AccessFeature PaperArticle
Overview of Binary Locally Repairable Codes for Distributed Storage Systems
Electronics 2019, 8(6), 596; https://doi.org/10.3390/electronics8060596 - 28 May 2019
Cited by 3
Abstract
This paper summarizes the details of recently proposed binary locally repairable codes (BLRCs) and their features. The construction of codes over a small alphabet size of symbols is of particular interest for efficient hardware implementation. Therefore, BLRCs are highly noteworthy because no multiplication [...] Read more.
This paper summarizes the details of recently proposed binary locally repairable codes (BLRCs) and their features. The construction of codes over a small alphabet size of symbols is of particular interest for efficient hardware implementation. Therefore, BLRCs are highly noteworthy because no multiplication is required during the encoding, decoding, and repair processes. We explain the various construction approaches of BLRCs such as cyclic code based, bipartite graph based, anticode based, partial spread based, and generalized Hamming code based techniques. We also describe code generation methods based on modifications for linear codes such as extending, shorting, expurgating, and augmenting. Finally, we summarize and compare the parameters of the discussed constructions. Full article
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Open AccessArticle
Discriminative Sparsity Graph Embedding for Unconstrained Face Recognition
Electronics 2019, 8(5), 503; https://doi.org/10.3390/electronics8050503 - 07 May 2019
Cited by 3
Abstract
In this paper, we propose a new dimensionality reduction method named Discriminative Sparsity Graph Embedding (DSGE) which considers the local structure information and the global distribution information simultaneously. Firstly, we adopt the intra-class compactness constraint to automatically construct the intrinsic adjacent graph, which [...] Read more.
In this paper, we propose a new dimensionality reduction method named Discriminative Sparsity Graph Embedding (DSGE) which considers the local structure information and the global distribution information simultaneously. Firstly, we adopt the intra-class compactness constraint to automatically construct the intrinsic adjacent graph, which enhances the reconstruction relationship between the given sample and the non-neighbor samples with the same class. Meanwhile, the inter-class compactness constraint is exploited to construct the penalty adjacent graph, which reduces the reconstruction influence between the given sample and the pseudo-neighbor samples with the different classes. Then, the global distribution constraints are introduced to the projection objective function for seeking the optimal subspace which compacts intra-classes samples and alienates inter-classes samples at the same time. Extensive experiments are carried out on AR, Extended Yale B, LFW and PubFig databases which are four representative face datasets, and the corresponding experimental results illustrate the effectiveness of our proposed method. Full article
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Open AccessArticle
Optimal Virtual Machine Placement Based on Grey Wolf Optimization
Electronics 2019, 8(3), 283; https://doi.org/10.3390/electronics8030283 - 04 Mar 2019
Cited by 12
Abstract
Virtual machine placement (VMP) optimization is a crucial task in the field of cloud computing. VMP optimization has a substantial impact on the energy efficiency of data centers, as it reduces the number of active physical servers, thereby reducing the power consumption. In [...] Read more.
Virtual machine placement (VMP) optimization is a crucial task in the field of cloud computing. VMP optimization has a substantial impact on the energy efficiency of data centers, as it reduces the number of active physical servers, thereby reducing the power consumption. In this paper, a computational intelligence technique is applied to address the problem of VMP optimization. The problem is formulated as a minimization problem in which the objective is to reduce the number of active hosts and the power consumption. Based on the promising performance of the grey wolf optimization (GWO) technique for combinatorial problems, GWO-VMP is proposed. We propose transforming the VMP optimization problem into binary and discrete problems via two algorithms. The proposed method effectively minimizes the number of active servers that are used to host the virtual machines (VMs). We evaluated the proposed method on various VM sizes in the CloudSIM environment of homogeneous and heterogeneous servers. The experimental results demonstrate the efficiency of the proposed method in reducing energy consumption and the more efficient use of CPU and memory resources. Full article
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Open AccessArticle
An Enhanced Multi-Objective Gray Wolf Optimization for Virtual Machine Placement in Cloud Data Centers
Electronics 2019, 8(2), 218; https://doi.org/10.3390/electronics8020218 - 16 Feb 2019
Cited by 14
Abstract
Cloud computing offers various services. Numerous cloud data centers are used to provide these services to the users in the whole world. A cloud data center is a house of physical machines (PMs). Millions of virtual machines (VMs) are used to minimize the [...] Read more.
Cloud computing offers various services. Numerous cloud data centers are used to provide these services to the users in the whole world. A cloud data center is a house of physical machines (PMs). Millions of virtual machines (VMs) are used to minimize the utilization rate of PMs. There is a chance of unbalanced network due to the rapid growth of Internet services. An intelligent mechanism is required to efficiently balance the network. Multiple techniques are used to solve the aforementioned issues optimally. VM placement is a great challenge for cloud service providers to fulfill the user requirements. In this paper, an enhanced levy based multi-objective gray wolf optimization (LMOGWO) algorithm is proposed to solve the VM placement problem efficiently. An archive is used to store and retrieve true Pareto front. A grid mechanism is used to improve the non-dominated VMs in the archive. A mechanism is also used for the maintenance of an archive. The proposed algorithm mimics the leadership and hunting behavior of gray wolves (GWs) in multi-objective search space. The proposed algorithm was tested on nine well-known bi-objective and tri-objective benchmark functions to verify the compatibility of the work done. LMOGWO was then compared with simple multi-objective gray wolf optimization (MOGWO) and multi-objective particle swarm optimization (MOPSO). Two scenarios were considered for simulations to check the adaptivity of the proposed algorithm. The proposed LMOGWO outperformed MOGWO and MOPSO for University of Florida 1 (UF1), UF5, UF7 and UF8 for Scenario 1. However, MOGWO and MOPSO performed better than LMOGWO for UF2. For Scenario 2, LMOGWO outperformed the other two algorithms for UF5, UF8 and UF9. However, MOGWO performed well for UF2 and UF4. The results of MOPSO were also better than the proposed algorithm for UF4. Moreover, the PM utilization rate (%) was minimized by 30% with LMOGWO, 11% with MOGWO and 10% with MOPSO. Full article
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Open AccessArticle
A Model of an Oscillatory Neural Network with Multilevel Neurons for Pattern Recognition and Computing
Electronics 2019, 8(1), 75; https://doi.org/10.3390/electronics8010075 - 09 Jan 2019
Cited by 16
Abstract
The current study uses a novel method of multilevel neurons and high order synchronization effects described by a family of special metrics, for pattern recognition in an oscillatory neural network (ONN). The output oscillator (neuron) of the network has multilevel variations in its [...] Read more.
The current study uses a novel method of multilevel neurons and high order synchronization effects described by a family of special metrics, for pattern recognition in an oscillatory neural network (ONN). The output oscillator (neuron) of the network has multilevel variations in its synchronization value with the reference oscillator, and allows classification of an input pattern into a set of classes. The ONN model is implemented on thermally-coupled vanadium dioxide oscillators. The ONN is trained by the simulated annealing algorithm for selection of the network parameters. The results demonstrate that ONN is capable of classifying 512 visual patterns (as a cell array 3 × 3, distributed by symmetry into 102 classes) into a set of classes with a maximum number of elements up to fourteen. The classification capability of the network depends on the interior noise level and synchronization effectiveness parameter. The model allows for designing multilevel output cascades of neural networks with high net data throughput. The presented method can be applied in ONNs with various coupling mechanisms and oscillator topology. Full article
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