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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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Exploiting Frame Similarity for Efficient Inference on Edge devices 42

Exploiting Frame Similarity for Efficient Inference on Edge ...

引用

42nd IEEE International conference on Distributed computing Systems (ICDCS)

作者： Ying, Ziyu Zhao, Shulin Zhang, Haibo Mishra, Cyan Subhra Bhuyan, Sandeepa Kandemir, Mahmut T. Sivasubramaniam, Anand Das, Chita R. Penn State Univ Dept Comp Sci & Engn University Pk PA 16802 USA Penn State University Pk PA USA

ISBN: (数字)9781665471770

ISBN: (纸本)9781665471770

Deep neural networks (DNNs) are being widely used in various computer vision tasks as they can achieve very high accuracy. However, the large number of parameters employed in DNNs can result in long inference times for vision tasks, thus making it even more challenging to deploy them in the compute- and memory-constrained mobile/edge devices. To boost the inference of DNNs, some existing works employ compression (model pruning or quantization) or enhanced hardware. However, most prior works focus on improving model structure and implementing custom accelerators. As opposed to the prior work, in this paper, we target the video data that are processed by edge devices, and study the similarity between frames. Based on that, we propose two runtime approaches to boost the performance of the inference process, while achieving high accuracy. Specifically, considering the similarities between successive video frames, we propose a frame-level compute reuse algorithm based on the motion vectors of each frame. With frame-level reuse, we are able to skip 53% of frames in inference with negligible overhead and remain within less than 1% mAP (accuracy) drop for the object detection task. Additionally, we implement a partial inference scheme to enable region/tile-level reuse. Our experiments on a representative mobile device (Pixel 3 Phone) show that the proposed partial inference scheme achieves 2x speedup over the baseline approach that performs full inference on every frame. We integrate these two data reuse algorithms to accelerate the neural network inference and improve its energy efficiency. More specifically, for each frame in the video, we can dynamically select between (i) performing a full inference, (ii) performing a partial inference, or (iii) skipping the inference altogether. Our experimental evaluations using six different videos reveal that the proposed schemes are up to 80% (56% on average) energy efficient and 2.2 x performance efficient compared to the conventi

关键词： Mobile computing DNN Inference Energy Efficient Video Analysis Motion Vector Object Detection

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Testbed and Experiments for Quantum-Conventional Networking

Testbed and Experiments for Quantum-Conventional Networking

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Quantum Communications, Networking, and computing (QCNC), International conference on

作者： Nageswara S. V. Rao Muneer Alshowkan Joseph C. Chapman Hsuan-Hao Lu Nicholas A. Peters Joseph M. Lukens Guoliang Xue Saikat Guha Emily Van Milligen Don Towsley Gayane Vardoyan Leonardo Bacciottini Linran Fan Oak Ridge National Laboratory Oak Ridge USA Purdue University West Lafayette IN USA Arizona State University Tempe AZ USA University of Maryland College Park MD USA University of Massachusetts Amherst MA USA University of Texas Austin TX USA

ISBN: (数字)9798331531591

ISBN: (纸本)9798331531607

The realization of quantum networks requires the development of devices and methods unprecedented in conventional networks, and yet they critically depend on the latter for implementing foundational blocks and essential operations. We describe a testbed to support the development and testing of their functionality and performance by providing quantum and conventional data planes and devices, together with a secure conventional control plane. It incorporates a variety of entangled photon sources, qubit technologies, detector technologies, photonic components, and supporting conventional switches and workstations. It implements a novel fiber telescoping scheme that provides suites of connections using fiber spools and inground-aerial fiber loops. We briefly summarize a variety of experiments conducted over this testbed including: (i) flex-grid quantum connection experiments, (ii) quantum state and channel tomography, (iii) utilization of quantum key distribution keys to secure conventional encryption and firewall devices, (iv) comparative study of analytical capacity estimates and entanglement throughput, (v) deployed squeezing coexisting with conventional communications, and (iv) measurement of polarization time variation.

关键词： Optical fiber polarization Performance evaluation Quantum entanglement Optical fiber networks Tomography Optical fiber devices Workstations Synchronization Optical fiber testing photonics

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A Software-Defined Approach to Enabling Network Controllers for Smart Environment Digital Twins

A Software-Defined Approach to Enabling Network Controllers ...

引用

IEEE International conference on Cloud computing Technology and Science (CloudCom)

作者： Chih–Chun Wu Cheng–Chia Lai Nalini Venkatasubramanian Cheng-Hsin Hsu Department of Computer Science National Tsing Hua University Taiwan Department of Computer Science University of California Irvine USA

ISBN: (数字)9798331507589

ISBN: (纸本)9798331507596

The rapid deployment of Internet-of-Things (IoT) devices in smart environments such as smart campuses and cities necessitates robust Quality-of-Service (QoS) management across heterogeneous networks. In this paper, we extend the concept of Network Digital Twin (NDT) to networked IoT devices, presenting the Network Digital Twin Controller (NDTC) that enhances the functionality and performance of smart environ-ments. Our NDTC addresses key challenges by creating Digital Twins (DTs) of Physical Twins (PTs), synchronizing their states, and performing QoS-related what-if analyses. Specifically, we built a DT-enabled IoT-instrumented smart environment using an open-source Software-Defined Network (SDN) controller. We formulated and solved the state synchronization problem using our proposed Optimal Update (OU) and Gradient-driven Update (GU) algorithms, carefully adjusting the update frequency and data granularity to minimize DT/PT state deviation within given network bandwidth budgets. We also formulated and addressed the what-if analysis problem by selecting optimal what-if analyzers using our Optimal Selection (OS) algorithm for the most accurate QoS predictions under a given computing time budget. Our extensive experiments on a real testbed demonstrated the merits of our proposed solution: (i) our developed NDTC and algorithms meet the functional requirements, (ii) our OU and GU algorithms significantly reduce the state deviation between PTs and DTs, and (iii) our OS algorithm largely reduces the prediction errors of what-if analyses.

关键词： Performance evaluation Cloud computing Heuristic algorithms Quality of service Bandwidth Prediction algorithms Digital twins Synchronization Internet of Things Frequency synchronization

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Meta-MADDPG: Achieving Transfer-Enhanced MEC Scheduling via Meta Reinforcement Learning 17th

Meta-MADDPG: Achieving Transfer-Enhanced MEC Scheduling via ...

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

作者： Yao, Yiming Ren, Tao Cui, Meng Liu, Dong Niu, Jianwei Beihang Univ Hangzhou Innovat Inst Hangzhou 310051 Peoples R China CNPC Engn Technol R&D Co Ltd Beijing 102206 Peoples R China Henan Normal Univ Coll Comp & Informat Engn Xinxiang 453007 Henan Peoples R China Beihang Univ Sch Comp Sci & Engn State Key Lab Virtual Real Technol & Syst Beijing 100191 Peoples R China

ISBN: (纸本)9783031192104;9783031192111

With the assistance of mobile edge computing (MEC), mobile devices (MDs) can optionally offload local computationally heave tasks to edge servers that are generally deployed at the edge of networks. As thus, the latency of task and energy consumption of MDs can be both reduced, significantly improving mobile users' quality of experience. Although considerable MEC scheduling algorithms have been designed by researchers, most of them are trained to solve specific tasks, leaving the performance in other MEC environments remaining dubious. To address the issue, this paper first formulates the optimization problem to minimize both task delay and energy consumption, and then transforms it into Markov decision problem that is further solved by using the state-of-the-art multi-agent deep reinforcement learning method, i.e., MADDPG. Furthermore, aiming at improving the overall performance in various MEC environments, we integrate MADDPG with meta-learning and propose Meta-MADDPG which is carefully designed with dedicated reward functions. The evaluation results are given to showcase the more satisfactory performances of Meta-MADDPG over the state-of-the-art algorithms when confronting new environments.

关键词： Deep reinforcement learning Multi-agent deep deterministic policy gradient Meta learning Mobile edge computing

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QoE and Reliability-Aware Task Scheduling for Multi-user Mobile-Edge computing 17th

QoE and Reliability-Aware Task Scheduling for Multi-user Mob...

引用

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

作者： Jiang, Weiming Zhou, Junlong Cong, Peijin Zhang, Gongxuan Hu, Shiyan Nanjing Univ Sci & Technol Sch Comp Sci & Engn Nanjing Peoples R China Univ Southampton Sch Elect & Comp Sci Southampton Hants England

ISBN: (纸本)9783031192104;9783031192111

Mobile-edge computing (MEC) has become a popular research topic from both academia and industry since it can alleviate the computation and power limitations of mobile devices by offloading computation-intensive and energy-consuming tasks from mobile users to nearby edge servers for remote execution. Existing papers have studied related problems, however, none of them considers the reliability of MEC systems that may suffer soft errors during execution and bit errors during offloading. In this work, we study the task offloading and scheduling problem targeting to maximize the quality of experience (QoE) of multi-user MEC systems under a certain reliability requirement. We propose to decompose the original problem into i) a task offloading optimization problem, ii) a task-to-server assignment problem for ensuring system reliability constraint, and iii) a computing resource allocation problem for maximizing system QoE. To address these sub-problems, we first obtain the optimal offloading decision using the discrete particle swarm optimization method. We then propose a reliability-optimality analysis-based task assignment heuristic and a utility-optimal resource allocation algorithm. Simulation results show that our scheme outperforms two state-of-the-art approaches and two baseline methods. The average improvement on QoE (quantified by offloading utility) achieved by our scheme is up to 63.2% under reliability requirement.

关键词： MEC QoE Reliability Scheduling Resource allocation

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A Programmable photonic Chip for High-dimensional Quantum computing

A Programmable Photonic Chip for High-dimensional Quantum Co...

引用

2022 IEEE Region 10 International conference, TENCON 2022

作者： Huang, Jieshan Chi, Yulin Zhang, Zhanchuan Yang, Yan Gong, Qihuang Wang, Jianwei School of Physics Peking University State Key Laboratory for Mesoscopic Physics Beijing China Institute of Microelectronics Chinese Academy of Sciences Beijing China

ISBN: (数字)9781665450959

ISBN: (纸本)9781665450959

Controlling and programming quantum devices to process quantum information by the unit of qudit provides the possibilities for noise-resilient quantum communications, delicate quantum molecular simulations, and efficient quantum computations, showing great potential to enhance the capabilities of qubit-based quantum technologies. Here, we report a programmable high-dimensional quantum processor in a silicon-photonic in-tegrated circuit, which monolithically integrates all the key functionalities and capabilities of initialisation, manipulation, and measurement of the two quantum quart (ququart) states, and demonstrate its enhancement of quantum computational paral-lelism. By reprogramming the processor, we implemented the basic quantum Fourier transform algorithms, all in quaternary, to benchmark the enhancement of quantum parallelism using qudits. The monolithic integration and high programmability have allowed the implementations of more than one million high-fidelity preparations, operations and projections of qudit states in the processor. Our work shows an integrated photonic quantum technology for qudit-based quantum computing with enhanced capacity, accuracy, and efficiency, which could lead to the acceleration of building a large-scale Quantum computer. © 2022 IEEE.

关键词： Silicon photonics

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A Cross-Platform Immersive 3D Environment for Algorithm Learning 10th

A Cross-Platform Immersive 3D Environment for Algorithm Lear...

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10th conference on Cloud computing, Big Data and Emerging Topics (JCC-BD and ET)

作者： Dapoto, Sebastian Cristina, Federico Lascano, Gamal Thomas, Pablo Pesado, Patricia Univ Nacl La Plata III LIDI La Plata Buenos Aires Argentina Univ Nacl La Plata Fac Informat La Plata Buenos Aires Argentina

ISBN: (纸本)9783031145995;9783031145988

The educational field must adapt itself to changes and new ways of learning. M-learning proposes modern methods of supporting the learning process through the use of mobile devices that allow students to access study tools and materials at any time and place. In particular, in the initial levels of computer science careers, these types of tools facilitate the construction of knowledge and develop students' ability to solve problems, using flexible platforms that promote self-learning. The development of mobile applications with three-dimensional (3D) environments that make use of virtual reality, increase the interest of students in the use of learning support tools, and encourage the resolution of the proposed problems. This paper presents a novel 3D mobile application with virtual reality to support the learning of basic algorithms.

关键词： M-learning 3D mobile applications Virtual reality Unity Basic algorithm learning

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Joint Dynamic Pricing and computing Offloading in Edge-to-Cloud Collaboration

Joint Dynamic Pricing and Computing Offloading in Edge-to-Cl...

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International conference on Computer Supported Cooperative Work in Design

作者： Tong Yin Xin Chen Libo Jiao Jiaxuan Liao School of Computer Science Beijing Information Science & Technology University Beijing China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

With the continuous development of integrated satellite ground networks, edge servers are laid out on low orbit earth (LEO) satellites to provide seamless services for some remote areas. In order to further improve service quality for mobile devices, the collaborative work between edge and cloud has received widespread attention. In this article, we consider the scenario of insufficient base station (BS) coverage in remote areas and investigate a hybrid model of computing offloading and resource allocation for edge cloud collaborative computing, in which edge servers with limited resources collaborate with the cloud by purchasing cloud computing resources. In this way, cloud server (CS) and edge servers separately price their computing and storage resources to stimulate each other to participate in cooperation and maximize their respective utility. We jointly optimize the size of offloading tasks, offloading strategies for devices and resource pricing issues for edge servers and CS based on comprehensive consideration of price cost, energy cost and latency limitation. A multi-level Stackelberg game is formulated among the CS (leader), BSs (followers) and satellites (followers) in level I-II, the BSs (leaders), satellites (leaders) and IoT devices (followers) in level II-iii. Furthermore, we prove the existence and uniqueness of Stackelberg equilibrium (SE) in Stackelberg game. The SERI algorithm is proposed and simulations is conducted to show that SERI algorithm has good convergence performance and better entity utility.

关键词： Cloud computing Satellites Costs Computational modeling Heuristic algorithms Collaboration Games

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Data AUDIT: Identifying Attribute Utility- and Detectability-Induced Bias in Task Models 26th

Data AUDIT: Identifying Attribute Utility- and Detectability...

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26th International conference on Medical Image computing and Computer-Assisted Intervention (MICCAI)

作者： Pavlak, Mitchell Drenkow, Nathan Petrick, Nicholas Farhangi, Mohammad Mehdi Unberath, Mathias Johns Hopkins Univ Baltimore MD 21205 USA US FDA Ctr Devices & Radiol Hlth Silver Spring MD 20993 USA

ISBN: (纸本)9783031438974;9783031438981

To safely deploy deep learning-based computer vision models for computer-aided detection and diagnosis, we must ensure that they are robust and reliable. Towards that goal, algorithmic auditing has received substantial attention. To guide their audit procedures, existing methods rely on heuristic approaches or high-level objectives (e.g., non-discrimination in regards to protected attributes, such as sex, gender, or race). However, algorithms may show bias with respect to various attributes beyond the more obvious ones, and integrity issues related to these more subtle attributes can have serious consequences. To enable the generation of actionable, data-driven hypotheses which identify specific dataset attributes likely to induce model bias, we contribute a first technique for the rigorous, quantitative screening of medical image datasets. Drawing from literature in the causal inference and information theory domains, our procedure decomposes the risks associated with dataset attributes in terms of their detectability and utility (defined as the amount of information the attribute gives about a task label). To demonstrate the effectiveness and sensitivity of our method, we develop a variety of datasets with synthetically inserted artifacts with different degrees of association to the target label that allow evaluation of inherited model biases via comparison of performance against true counterfactual examples. Using these datasets and results from hundreds of trained models, we show our screening method reliably identifies nearly imperceptible bias-inducing artifacts. Lastly, we apply our method to the natural attributes of a popular skin-lesion dataset and demonstrate its success. Our approach provides a means to perform more systematic algorithmic audits and guide future data collection efforts in pursuit of safer and more reliable models. Full code is available at https://github. com/mpavlak25/data-audit.

关键词： Bias shortcut learning fairness algorithmic auditing datasets

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Soft Actor-Critic-Based DAG Tasks Offloading in Multi-access Edge computing with Inter-user Cooperation 21st

Soft Actor-Critic-Based DAG Tasks Offloading in Multi-access...

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

作者： Liu, Pengbo Ge, Shuxin Zhou, Xiaobo Zhang, Chaokun Li, Keqiu Tianjin Univ Coll Intelligence & Comp Tianjin Key Lab Adv Networking Tianjin 300350 Peoples R China

ISBN: (纸本)9783030953911;9783030953904

Multi-access edge computing (MEC) enables mobile applications, which consists of multiple dependent subtasks, to be executed in full parallelism between edge servers and mobile devices. The idea is to offload some of the subtasks to edge servers while utilizing the directed acyclic graphs (DAGs) based subtask dependency. In some cases involving multiple users running the same application, users can cooperate to improve the application's performance (e.g., object detection accuracy in connected and autonomous vehicles) by sharing the intermediate results of the subtasks, which is referred to as cooperation gain. However, inter-user cooperation also introduces additional dependencies between the subtasks of different users, which inevitably increases the execution delay of the application with DAG tasks offloading. Therefore, how to jointly optimize execution delay and cooperation gain remains an open question. In this paper, we present an approach for DAG tasks offloading in MEC with inter-user cooperation to optimize the execution delay of the application and the cooperation gain. First, we introduce the concept of application utility, which incorporates both delay and cooperation gain. We formulate the DAG tasks offloading problem with inter-user cooperation as a Markov decision process (MDP) to maximize the total application utility. Next, we propose a quantized soft actor-critic (QSAC) algorithm to solve the formulated problem. Specifically, QSAC generates multiple potential solutions according to the order-preserving quantizing method, increasing the exploration efficiency to avoid falling into local optimal while maintaining the overall stability. Finally, simulation results validate that the proposed algorithm significantly improves the total application utility compared with the other benchmark algorithms.

关键词： Multi-access edge computing (MEC) Tasks offloading Directed acyclic graphs (DAGs) Inter-user cooperation Markov decision process (MDP) Soft actor-critic (SAC)

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