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检索条件"任意字段=Conference on Photonic Devices and Algorithms for Computing III"

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A Dependency-Aware Task Offloading Strategy in Mobile Edge computing Based on Improved NSGA-II 17th

A Dependency-Aware Task Offloading Strategy in Mobile Edge C...

引用

17th International conference on Wireless algorithms, Systems, and Applications (WASA)

作者： Zhou, Chunyue Zhang, Mingxin Gao, Qinghe Jing, Tao Beijing Jiaotong Univ Sch Elect & Informat Engn Beijing Peoples R China

ISBN: (纸本)9783031192104;9783031192111

Rapid development of mobile communications has led to respectable latency-sensitive and computation-intensive mobile applications. There is a huge contradiction between high resource demands of these applications and limited resource of mobile devices. In this regard, mobile edge computing (MEC) is a promising technology, where computation tasks can be offloaded from mobile devices onto network edges with stronger capability. However, the dependency between tasks leads to high complexity for offloading decision. In this paper, we investigate the optimal offloading problem for completing dependency-aware tasks by minimizing the latency and energy cost. An improved non-dominated sorting genetic algorithm-II (INSGA-II) is proposed to solve this multiobjective problem. Simulation results validate the advantage of the proposed algorithm in terms of the performance of low latency and cost.

关键词： Mobile edge computing (MEC) Task dependency NSGA-II

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Subthreshold Swing Performance Assessment of Nanoscale Junctionless GAA FETs using Machine Learning Approach 21

Subthreshold Swing Performance Assessment of Nanoscale Junct...

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21st International conference on Electrical Engineering, computing Science and Automatic Control, CCE 2024

作者： Ouchen, R. Berghout, T. Djeffal, F. Ferhati, H. University of Batna 2 LEA Batna Algeria University of Batna 2 LAME Batna Algeria University of Larbi Ben m'Hidi ISTA Oum El Bouaghi Algeria

ISBN: (纸本)9798350377545

In this work, the current-voltage (I-V) characteristics of nanoscale Junctionless Gate-All-Around (JLGAA) Field-Effect Transistor (FET) structures, with and without high-k dielectric materials, were investigated using the ATLAS 2D simulator. Various machine learning (ML) algorithms were employed to analyze and classify the design factors influencing one of the principal electrical parameters of the device, whixh is the subthreshold swing (SS) factor. The SS factor is a critical parameter in evaluating the performance of nanscale Junctionless GAA FE Ts, quantifying how effectively the transistor can switch from the off state (low current) to the on state (high current). Our approach aims to study and classify the impact of oxide thickness, channel radius, channe doping and high-k dielectric materials to better understund and enhance the gate control and achieve optimal performance. It was found that the proposed design can be classified into three categories based on SS values: Good SS ( 500 mV/dec). This classification of SS values in 20 nm-Junctionless GAA FET provides a framework for understanding the trade-offs between performances and design parameters. © 2024 IEEE.

关键词： iii-V semiconductors

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Risk-Aware Optimization of Distribution-Based Resilient Task Assignment in Edge computing 21st

Risk-Aware Optimization of Distribution-Based Resilient Task...

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21st International conference on algorithms and Architectures for Parallel Processing (ICA3PP)

作者： Jin, Hang Liang, Jiayue Liu, Fang Hunan Univ Changsha Peoples R China Sun Yat Sen Univ Guangzhou Peoples R China

ISBN: (纸本)9783030953911;9783030953904

computing resources of mobile devices are growing, and unoccupied resources can be shared to provide support for edge computing services in edge clouds. Unlike stable servers, a significant challenge is that mobile devices may exit or join an edge cloud at any time due to change of position. This dynamic nature of mobile devices may result in abortions of task execution. In this paper, a risk-aware task assignment scheme called RATA is proposed. RATA minimizes the overhead caused by potential abortions of task execution by prioritizing tasks to the edge nodes which are unlikely to exit during task execution. We first quantify the abortion risk of each task-node pair to an expected extra overhead time, and formulate a risk-aware task assignment problem that strives to minimize the average completion time of all tasks, as well as the expected extra overhead time of each task. We then design a novel task assignment scheme to solve this problem with genetic algorithm. Finally, we implement a prototype system to evaluate the performance. The experimental results show that our scheme outperforms the state-of-art task assignment schemes in terms of average completion time and deadline miss rate in most cases.

关键词： Task assignment Task scheduling Edge computing Risk-aware Mobile device

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Cooperative Offloading Based on Online Auction for Mobile Edge computing 17th

Cooperative Offloading Based on Online Auction for Mobile Ed...

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17th International conference on Wireless algorithms, Systems, and Applications (WASA)

作者： Zheng, Xiao Shah, Syed Bilal Hussain Nawaf, Liqaa Rana, Omer F. Zhu, Yuanyuan Gan, Jianyuan Shandong Univ Technol Sch Comp Sci & Technol Zibo Peoples R China Manchester Metropolitan Univ Sch Comp & Math Manchester Lancs England Cardiff Metropolitan Univ Comp Sci Sch Technol Cardiff S Glam Wales Cardiff Univ Sch Comp Sci & Informat Cardiff S Glam Wales Dalian Univ Technol Sch Software Dalian 116620 Peoples R China

ISBN: (纸本)9783031192104;9783031192111

In the field of edge computing, collaborative computing offloading, in which edge users offload tasks to adjacent mobile devices with rich resources in an opportunistic manner, provides a promising example to meet the requirements of low latency. However, most of the previous work has been based on the assumption that these mobile devices are willing to serve edge users, with no incentive strategy. In this paper, an online auction-based strategy is proposed, in which both users and mobile devices can interact dynamically with the system. The auction strategy proposed in this paper is based on an online approach to optimize the long-term utility of the system, such as start time, length and size, resource requirements, and evaluation valuation, without knowing the future. Experiments verify that the proposed online auction strategy achieves the expected attributes such as individual rationality, authenticity and computational ease of handling. In addition, the index of theoretical competitive ratio also indicates that the proposed online mechanism realizes near-offline optimal long-term utility performance.

关键词： Online auction strategy Collaborative computing offloading Long-term utility

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Artificial Intelligence Using Complex photonics: Decision Making and Reservoir computing

Artificial Intelligence Using Complex Photonics: Decision Ma...

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Optical Fiber Communication (OFC) conference

作者： Atsushi Uchida Department of Information and Computer Sciences Saitama University Saitama City Saitama Japan

We overview recent development on photonic decision making and reservoir computing for artificial intelligence using complex photonics. Parallel implementations of photonic devices can accelerate information processin... 详细信息

ISBN: (纸本)9781957171180

关键词： Machine learning algorithms Chaotic communication Semiconductor lasers Scalability Decision making Reservoirs Laser modes

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High-speed AI image space wavefront sensing using embedded computing: achieving 1000 frames per second 12

High-speed AI image space wavefront sensing using embedded c...

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2025 conference on photonic Instrumentation Engineering

作者： Baudat, Gaston Lavanchy, David Muller, Guillaume Innovat Foresight LLC 4432 Mallard Point Columbus IN 47201 USA Univ Arizona Wyant Coll Opt Sci 1630 E. Univ Blvd Tucson AZ 85721 USA Univ Appl Sci Western Switzerland HEIG VD Route Cheseaux 1Case Postale 521 CH-CH1401 Yverdon Switzerland

ISBN: (纸本)9781510684942;9781510684959

Wavefront sensing is fundamental to numerous scientific and industrial applications in optics, particularly in adaptive optics (AO). This paper presents a high-speed image space AI-powered wavefront sensing system, based on a patented technology known as AI4Wave, that eliminates the need for dedicated wavefront sensors, such as Shack-Hartmann, relying solely on fast cameras and AI edge computing devices, specifically an NVIDIA Jetson embedded module in our implementation. This module could also be utilized in the future to directly control actuators, streamlining the entire AO process. The system captures defocused images of a point source or similar, which are processed by a feedforward artificial neural network (NN) trained exclusively on synthetic data [1]. This approach enables real-time phase retrieval, overcoming the local derivative limitations of Shack-Hartmann sensors and handling large wavefront or surface errors spanning multiple wavelengths. It also provides a unique capability for field-dependent wavefront measurement from a single image of multiple sources. Yu Wu et al. [2] proposed a sub-millisecond phase retrieval method that requires two simultaneous images for optimal performance and NN training on physical data. However, this approach necessitates retraining whenever system parameters, such as f/#, wavelength, or pixel size, are altered. In contrast, AI4Wave employs synthetic, normalized data that is agnostic to most optical layout changes, enabling robust and versatile training and validation. Combined with off-the-shelf NVIDIA hardware, it offers a ready-to-use solution for industrial deployment. By integrating optimized algorithms within modern AI acceleration frameworks, such as NVIDIA TensorRT, and leveraging advanced AI edge computers, the system achieves processing speeds of 1000 frames per second or more, effectively providing sub-millisecond wavefront sensing capability. The minimal hardware setup, consisting only of a camera and an NVIDI

关键词： Cameras Education and training Monochromatic aberrations Wavefront sensors Wavefronts Phase retrieval Adaptive optics Image retrieval Point spread functions Sensors

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Machine Learning-Driven Analysis and Optimization of Optical Ring Resonator for Integrated photonic Applications

Machine Learning-Driven Analysis and Optimization of Optical...

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Smart Systems for applications in Electrical Sciences (ICSSES), International conference on

作者： Chandrika T N Nischal S Ruqaiah Sadiya SanjayKumar B S Nithyashree M Department of ETE Siddaganga Institute of Technology Tumkur India

ISBN: (数字)9798331507671

ISBN: (纸本)9798331507688

Ring resonators are pivotal components in photonics, enabling light manipulation for applications in telecommunications, optical sensing, and quantum computing. Integrating machine learning with photonic design opens new avenues for enhancing the performance and efficiency of these devices. In this study, we leverage the Lumerical software to create a comprehensive database of ring resonator designs and their performance metrics. Utilizing the $\mathbf{k}$ -Nearest Neighbors (KNN) regression algorithm, we develop a predictive model to optimize resonator parameters for specific applications. This approach allows for rapid exploration of the design space, uncovering innovative resonator configurations and improving sensitivity and efficiency. The proposed method demonstrates the potential of machine learning to accelerate photonic device development, offering a systematic, data-driven strategy for advancing ring resonator technology. Obtained accuracy of 82.8

关键词： Adaptation models Accuracy Computational modeling Refractive index Machine learning Nearest neighbor methods Predictive models Prediction algorithms Optical ring resonators photonics

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A Comparative Study on Single-handed Keyboards on Large-screen Mobile devices 2022

A Comparative Study on Single-handed Keyboards on Large-scre...

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16th Working conference on Advanced Visual Interfaces (ACM AVI)

作者： Zhang, Kunpeng Deng, Zhigang Univ Houston Dept Comp Sci Houston TX 77004 USA

ISBN: (纸本)9781450397193

Many questions regarding single-hand text entry on modern smart-phones (in particular, large-screen smartphones) remain under-explored, such as, (i) will the existing prevailing single-handed keyboards fit for large-screen smartphone users? and (ii) will individual-customization improve single-handed keyboard performance? In this paper we study single-handed typing behaviors on several representative keyboards on large-screen mobile devices. We found that, (i) the user-adaptable-shape curved keyboard performs best among all the studied keyboards;(ii) users' familiarity with the Qwerty layout plays a significant role at the beginning, but after several sessions of training, the user-adaptable curved keyboard can have the best learning curve and performs best;(iii) generally the statistical decoding algorithms via spatial and language models can well handle the input noise from single-handed typing.

关键词： Mobile computing keyboard interfaces single-handed typing comparative study individual customization

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Processing Power-based Task Scheduling Technique for Cloud-Fog Environment 17

Processing Power-based Task Scheduling Technique for Cloud-F...

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17th conference on Industrial Electronics and Applications (ICIEA)

作者： Lepcha, Norbu Z. Halgamuge, Malka N. Vel Tech Rangarajan Dr Sagunthala R&D Inst Sci & Comp Sci & Engn Chennai Tamil Nadu India RMIT Univ Dept Informat Syst & Business Analyt Melbourne Vic 3000 Australia

ISBN: (纸本)9781665409841

Internet of Things (IoT) applications have emerged as a critical component in improving people's quality of life. The increasing volume of data generated by IoT devices, on the other hand, places a strain on the resources of traditional cloud data centres. This makes cloud data centres unable to meet the demands of IoT applications. Due to resource constraints and the massive connectivity of IoT devices, efficient task (incoming request) allocation is critical for service providers. We create a novel processing power-based task scheduling technique that employs a broker manager to distribute tasks and the process strategy across multiple devices. This is important in cloud technology because it enables organisations to effectively scale their processes. We use multithreading to parallelize processes and evaluate the performance of the proposed model using existing task allocation algorithms (i) round-robin, (ii) weighted round-robin, (iii) random allocation, (iv) genetic algorithm, and (v) dynamic algorithm. Our results show that when there are few tasks and one cluster has more processing capacity than the others, a weighted round-robin algorithm works well. The effectiveness of allocating based on weight shows no improvement in task completion time as task size increases. Using our proposed model, we show that the dynamic algorithm outperforms all other algorithms for task scheduling by 1.37 times for all task sizes.

关键词： Task Scheduling resource allocation IoT applications dynamic algorithm cloud-fog computing

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Synchronous Federated Learning Latency Optimization Based on Model Splitting 17th

Synchronous Federated Learning Latency Optimization Based on...

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17th International conference on Wireless algorithms, Systems, and Applications (WASA)

作者： Fang, Chen Shi, Lei Shi, Yi Xu, Jing Ding, Xu Hefei Univ Technol Sch Comp Sci & Informat Engn Hefei 230009 Peoples R China Virginia Tech Dept ECE Blacksburg VA 24061 USA Minist Educ Engn Res Ctr Safety Crit Ind Measurement & Contro Hefei 230009 Peoples R China

ISBN: (纸本)9783031192104;9783031192111

Federated Learning (FL) is a distributed machine learning approach which is suitable for edge computing environment. While in this environment, how to take full advantage of the computing resources on end devices and edge servers is still a difficult problem. Especially for the synchronous federated learning, computing resources among different participants will lead to extra time cost and cause resource waste. In this paper, we try to reduce the time cost and the computing resource waste by using model splitting and task scheduling. We first establish the mathematical model and find it can not be solved directly. Then we design our algorithm which we name as the Federated Learning Offloading Acceleration (FLOA) algorithm to obtain a sub-optimal solution. The FLOA algorithm first uses the Partition Points Selection method to reduce the size of the solution space, then proposes a task offloading method based on matching theory. Experiments and simulations show that compared to the other three calculation methods, the single iteration time is reduced by 47%, 28%, 14% under our algorithm in turn.

关键词： Edge computing Federated learning Model splitting

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