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

共 215 条记录，以下是51-60 订阅

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Triply-Resonant Sum-Frequency Generation in 10 µm Gallium Phosphide photonic Ring Resonators

Triply-Resonant Sum-Frequency Generation in 10 µm Gallium P...

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Frontiers in Optics + Laser Science 2021, FiO+LS 2021 - Part of Frontiers in Optics, FiO 2021

作者： Logan, Alan D. Shree, Shivangi Hestroffer, Karine Hatami, Fariba Fu, Kai-Mei C. Department of Electrical and Computing Engineering University of Washington SeattleWA98195 United States Department of Physics University of Washington SeattleWA98195 United States Department of Physics Humboldt-Universitat zu Berlin Berlin12489 Germany

We demonstrate triply-resonant sum-frequency generation from telecom to visible in a gallium phosphide-on-oxide integrated photonic platform. Multi-resonant devices are essential for scalable low-power frequency conve... 详细信息

ISBN: (纸本)9781557528209

关键词： iii-V semiconductors

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Low-Latency and Energy-Efficient Task Scheduling for End-Edge-Cloud Collaborative computing

Low-Latency and Energy-Efficient Task Scheduling for End-Edg...

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IEEE International conference on Systems, Man and Cybernetics

作者： Jingyao Li Haitao Yuan Yaofei Ma Jing Bi Jinhong Yang Jia Zhang School of Automation Science and Electrical Engineering Beihang University Beijing China College of Computer Beijing University of Technology Beijing China CSSC Systems Engineering Research Institute Beijing China Dept. of Computer Science Southern Methodist University Dallas TX USA

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

Mobile edge computing (MEC) is a new paradigm that improves the quality of service compared with traditional cloud computing. In MEC, computational tasks are submitted by numerous end users and are partially offloaded to edge servers or a central cloud. However, the characteristics of tasks are different from each other, and the limited resources of computational nodes are also heterogeneous, which brings great challenges to computation offloading and resource allocation for MEC. This work establishes an end-edge-cloud collaborative computing network, which consists of end devices, edge servers, and a central cloud. Task execution location and CPU running frequency determine the execution time and energy consumption to finish the tasks. Considering the aforementioned factors, a multi-objective constrained optimization problem is formulated. To solve the problem, an improved Non-dominated Sorting Genetic Algorithm II (NSGA-II) with self-adaptive crossover and mutation rates is proposed, which is called Improved NSGA-II with _Self-adaptive Crossover and Mutation (INSCM). The total execution time and energy consumption can be jointly minimized with our proposed INSCM. Numerous experiments are carried out to test the performance of INSCM. Simulation results show that INSCM effectively improves the performance of NSGA-II and surpasses random offloading and NSGA-iii, which shows practical use in real-life scenarios.

关键词： Energy consumption Cloud computing Time-frequency analysis Multi-access edge computing Simulation Collaboration Servers Optimization Sorting Genetic algorithms

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A Reinforcement Model Based Prioritized Replay to Solve the Offloading Problem in Edge computing 16th

A Reinforcement Model Based Prioritized Replay to Solve the ...

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16th International conference on Wireless algorithms, Systems, and Applications (WASA)

作者： Liu, Wenrui Wang, Cong Mi, Jiazhi Luan, He Luo, Yixin Northeastern Univ Qinhuangdao Qinhuangdao Hebei Peoples R China

ISBN: (纸本)9783030861377;9783030861360

Mobile edge computing is widely applied to help mobile devices to improve its data processing speed. However, one of its main challenges is how to generate computation offloading decision effectively and quickly in the complex wireless scenario. In this paper, we aim to build up a multiple user devices application scenario, where each device performs binary computation offloading policy which is executed in the local device or offloaded to a cloud server via the wireless network. A model based on deep reinforcement learning is proposed to optimize computation offloading decisions. First, the weighted rate of offloading computation is introduced to be a reward in the Q function. Second, offloading decisions are generated from a deep Q-network (DQN) with batch normalization layers. At last, the deep Q-network is trained with a designed prioritized replay policy. Experimental results indicate the proposed model generates the optimal offloading decisions in a short time and gets faster convergence speed on the weighted rate.

关键词： Mobile edge computing Binary computation offloading Deep reinforcement learning Prioritized replay

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Optimal Mapping for Near-Term Quantum Architectures based on Rydberg Atoms 40

Optimal Mapping for Near-Term Quantum Architectures based on...

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40th IEEE/ACM International conference on Computer Aided Design (ICCAD)

作者： Brandhofer, Sebastian Buechler, Hans Peter Polian, Ilia Univ Stuttgart Ctr Integrated Quantum Sci & Technol Inst Comp Architecture & Comp Engn Stuttgart Germany Univ Stuttgart Ctr Integrated Quantum Sci & Technol Inst Theoret Phys 3 Stuttgart Germany

ISBN: (纸本)9781665445078

Quantum algorithms promise quadratic or exponential speedups for applications in cryptography, chemistry and material sciences. The topologies of today's quantum computers offer limited connectivity, leading to significant overheads for implementing such quantum algorithms. One-dimensional topology displacements that remedy these limits have been recently demonstrated for architectures based on Rydberg atoms, and they are possible in principle in photonic and ion trap architectures. We present the first optimal quantum circuit-to-architecture mapping algorithm that exploits such one-dimensional topology displacements. We benchmark our method on quantum circuits with up to 15 qubits and investigate the improvements compared with conventional mapping based on inserting swap gates into the quantum circuits. Depending on underlying technology parameters, our approach can decrease the quantum circuit depth by up to 58% and increase the fidelity by up to 29%. We also study runtime and fidelity requirements on one-dimensional displacements and swap gates to derive conditions under which one-dimensional topology displacements provide benefits.

关键词： Quantum computing Quantum Circuit Mapping Topology Displacements Rydberg Atoms CAD

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Online Task Scheduling for DNN-Based Applications over Cloud, Edge and End devices 16th

Online Task Scheduling for DNN-Based Applications over Cloud...

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16th International conference on Wireless algorithms, Systems, and Applications (WASA)

作者： Zhong, Lixiang Shi, Jiugen Shi, Lei Xu, Juan Fan, Yuqi Xu, Zhigang Hefei Univ Technol Sch Comp Sci & Informat Engn Hefei 230009 Peoples R China

ISBN: (纸本)9783030861377;9783030861360

As a combination of artificial intelligence (AI) and edge computing, edge intelligence has made great contributions in pushing AI applications to the edge of the network, especially in reducing delay, saving energy and improving privacy. However, most of researchers only considered the computation approach of end device to edge server and ignored the scheduling of multi-task. In this paper, we study DNN model partition and online task scheduling over cloud, edge and devices for deadline-aware DNN inference tasks. We first establish our mathematical model and find the model can not be solved directly because the solution space is too large. Therefore, we propose the partition point filtering algorithm to reduce the solution space. Then by jointly considering management of the networking bandwidth and computing resources, we propose our online scheduling algorithm to meet the maximum number of deadlines. Experiments and simulations show that our online algorithm reduces deadline miss ratio by up to 51% compared with other four typical computation approaches.

关键词： Edge computing Edge intelligence Model partition Task scheduling

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Parallelizing Stream Compression for IoT Applications on Asymmetric Multicores 39

Parallelizing Stream Compression for IoT Applications on Asy...

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39th IEEE International conference on Data Engineering, ICDE 2023

作者： Zeng, Xianzhi Zhang, Shuhao Istd Pillar Singapore University of Technology and Design Singapore Singapore

ISBN: (纸本)9798350322279

Data stream compression attracts much attention recently due to the rise of IoT applications. Thanks to the balanced computational power and energy consumption, asymmetric multicores are widely used in IoT devices. This paper introduces CStream, a novel framework for parallelizing stream compression on asymmetric multicores to minimize energy consumption without violating the user-specified compressing latency constraint. Existing works cannot effectively utilize asymmetric multicores for stream compression, primarily due to the non-trivial asymmetric computation and asymmetric communication effects. To this end, CStream is developed with the following two novel designs: 1) fine-grained decomposition, which decomposes a stream compression procedure into multiple fine-grained tasks to better expose the task-core affinities under the asymmetric computation effects;and 2) asymmetry-aware task scheduling, which schedules the decomposed tasks based on a novel cost model to exploit the exposed task-core affinities while considering asymmetric communication effects. To validate our proposal, we evaluate CStream with five competing mechanisms of parallelizing stream compression algorithms on a recent asymmetric multicore processor. We evaluate CStream with five competing mechanisms of parallelizing stream compression algorithms to validate our proposal on a recent asymmetric multicore processor. Our extensive experiments based on a benchmark of three algorithms and four datasets show that CStream outperforms alternative approaches by up to 53% lower energy consumption without compressing latency constraint violation. © 2023 IEEE.

关键词： Edge computing

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Remote Sensing: Leveraging Cloud IoT and AI/ML Services

Remote Sensing: Leveraging Cloud IoT and AI/ML Services

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conference on Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications iii

作者： Bennett, Kelly W. Robertson, James US Army Res Lab Sensors & Electron Devices Directorate Adelphi MD 20783 USA Clearhaven Technol LLC Severna Pk MD 21146 USA

ISBN: (数字)9781510643307

ISBN: (纸本)9781510643307

Artificial Intelligence/Machine Learning (AI/ML) services implemented at the tactical edge on multiple, distributed low power sensors can take advantage of Cloud IoT services and processes to learn in complex data environments supporting evolving mission tasks and continuous improvement of algorithms through Cloud automation and management. Remote sensors and their associated functionality must exhibit resilience against adversaries and deceptive techniques, and operate securely in all domains. Amazon Web Services (AWS) IoT Greengrass uses machine learning models that are built in the cloud and deployed locally on remote sensors and IoT devices. Limited datasets can be used to train models and be refined as more data is available. AWS SageMaker can be used for scene detection using Images and other signals of interest resulting in alerts and notifications from deployed sensors. The quality of machine learning models can be improved through captured data from IoT Greengrass being returned to the Cloud and processed by AWS SageMaker. Labeling of data can be streamlined through Cloud services such as Amazon SageMaker Ground Truth using auto-segment, automatic 3D cuboid snapping, and other automated labeling features. Using AWS Cloud security services in addition to AI/ML and IoT services provides additional advantages supporting identity and certificate management, sensor and data analytics, and intrusion detection and prevention. The number of low-cost sensors supporting Cloud-based IoT remote sensing using AI/ML algorithms at the edge continues to grow. Raspberry PI, NVIDIA Jetson Nano and others, equipped with appropriate sensor devices, are readily available for building a distributed remote sensing network and communicating with the Cloud. This paper provides a detailed description of components and the initial results of building a small distributed remote sensing network using NVIDIA Jetson Nano edge devices equipped with inexpensive acoustic and image sensors u

关键词： Amazon Web Services Cloud computing IoT AL/ML algorithms AWS AI/ML Services Big data Disruptive computing Technologies AWS Greengrass AWS SageMaker AWS PyTorch

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STEROCEN: Simulation and Training Environment for Resource Orchestration in Cloud-Edge Networks

STEROCEN: Simulation and Training Environment for Resource O...

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International conference on the Network of the Future (NOF)

作者： Mieszko Ferens Diego Hortelano Ignacio de Miguel Ramón J. Durán Barroso Sokol Kosta Aalborg University Copenhagen Denmark Universidad Rey Juan Carlos Madrid Spain Universidad de Valladolid Valladolid Spain

ISBN: (数字)9798350377767

ISBN: (纸本)9798350377774

Large scale deployment of Internet-of-Things (IoT) devices is projected to grow in the coming years. These devices are expected to be low-cost while supporting applications with growing computational demands. To enable the necessary computations, offloading of computational tasks to Edge and Cloud nodes is a fundamental technology. However, orchestration for such networks is a complex problem which affects both the network design and the decision system. To aid in solving this problem, simulation tools are essential for predicting the performance of networks in different conditions and under different orchestration policies. In this paper, we propose STEROCEN, a Cloud-Edge network resource orchestration simulation and training tool which allows for different configurations of up-to a four-layer network composed of: (i) end-device, (ii) Close Edge, (iii) Far Edge, and (iv) Cloud layers. Our tool collects delay metrics for flexibly defined applications, especially in regard to computation in the network nodes and including uncertainty in processing times. Additionally, the tool only needs the initial configuration and an independently defined orchestrator, allowing for testing of many strategies. As an example, we provide results of testing some Deep Reinforcement Learning (DRL) algorithms using the same training and simulation environment.

关键词： Training Measurement Cloud computing Uncertainty Computational modeling Network architecture Delays Servers Internet of Things Testing

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

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

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IEEE conference on Industrial Electronics and Applications (ICIEA)

作者： Norbu Z. Lepcha Malka N. Halgamuge Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Department of Information Systems and Business Analytics RMIT University Melbourne Australia

ISBN: (纸本)9781665409858

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： Performance evaluation Cloud computing Data centers Job shop scheduling Heuristic algorithms Clustering algorithms Dynamic scheduling

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Experimental Evaluation of RSA algorithms for SDN-Programmable VCSEL-Based S-BVT in High-Capacity and Cost-Efficient Optical Metro Networks 11th

Experimental Evaluation of RSA Algorithms for SDN-Programmab...

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11th EAI International conference on Broadband Communications, Networks, and Systems, BROADNETS 2020

作者： Martinez, Ricardo Casellas, Ramon Svaluto Moreolo, Michela Fabrega, Josep Maria Vilalta, Ricard Munoz, Raul Nadal, Laia Fernández Palacios, Juan Pedro López, Víctor Larrabeiti, David Otero, Gabriel Castelldefels Spain Telefónica I+D GTCO Madrid Spain Universidad Carlos III Madrid Spain

ISBN: (纸本)9783030687366

Future metro networks need to increase the transport capacity and improve the cost- and power-efficiency. These challenges are tackled by the EU-H2020 PASSION project exploiting dense photonic integration and cost-efficient optical technologies. Specifically, PASSION investigates a) modular sliceable bandwidth variable transceivers (S-BVTs) built upòn a set of both vertical cavity surface emitting lasers (VCSEL) and Coherent Receivers (CO-Rx);and b) hierarchical switching nodes in a flexigrid network. An SDN controller handles the network programmability where a key functionality is the path computation and resource selection to fulfil the connection requirements. This is conducted by the Routing and Spectrum Assignment (RSA) algorithms. The considered S-BVT transmitter imposes that each S-BVT VCSEL reaches up to 50 Gb/s. Thus, connections requesting higher bandwidth (e.g., 200 Gb/s) are accommodated over several optical flows. In this context, two RSA algorithms called co-routed (RSA-CR) and inversed multiplexed (RSA-IM) optical flows are proposed and compared. The RSA-CR enforces that all the connection’s optical flows are routed over the same spatial path;the RSA-IM relaxes this allowing the optical flows being set up over different spatial routes. The experimental evaluation, made upon dynamic traffic, aims at comparing both RSA algorithms performance according to the blocked bandwidth ratio, the average used of S-BVT devices, and the average setup time. © 2021, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Optical flows

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