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

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Entanglement-enhanced Quantum Reinforcement Learning: an Application using Single-Photons 5

Entanglement-enhanced Quantum Reinforcement Learning: an App...

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2024 International conference on Quantum computing and Engineering

作者： Gaspar, Joaquim M. Bergerault, Alexandre Apostolou, Vassilis Ricou, Arno Quandela F-91300 Massy France Inst Polytech Paris ENSTA Paris F-91120 Palaiseau France

ISBN: (纸本)9798331541378

Quantum Reinforcement Learning (QRL) is an emerging field with many novel approaches suitable for noisy intermediate-scale quantum (NISQ) devices being proposed recently. Projective Simulation (PS) is a classical machine learning method based on random walks in a Directed Acyclic Graph (DAG), and is mainly used to train agents in a Reinforcement Learning setup. This graph corresponds to the Episodic Compositional Memory (ECM) of the agent. For the quantum version of PS we use quantum walks of single photons in quantum optical circuits, which go through tunable components (beamsplitters and phase shifters). We call this specific model Quantum Optical Projective Simulation (QOPS). In this work, we propose a general framework to translate any 2-layer ECM to a quantum optical circuit and an algorithm for solving quantum strategic games using QOPS. Moreover, we introduce QOPS with an entangled state as input. The application that we are using to evaluate the performance of the framework is the Prisoner's Dilemma. We executed the implementation using Altair's noisy simulator, designed to emulate a single photon-based quantum processor by Quandela. The algorithm provided promising results even in the presence of noise, which makes it a strong candidate for runs on single-photon -based quantum processors.

关键词： quantum reinforcement learning quantum machine learning variational quantum algorithms photonic quantum computing projective simulation quantum games

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Optimal Schedule of Mobile Edge computing Under Imperfect CSI 18th

Optimal Schedule of Mobile Edge Computing Under Imperfect CS...

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18th International conference on algorithms and Architectures for Parallel Processing (ICA3PP)

作者： Jiao, Libo Yin, Hao Lyu, Yongqiang Huang, Haojun Dong, Jiaqing Guo, Dongchao Tsinghua Univ Beijing 100084 Peoples R China China Univ Geosci Wuhan 430072 Peoples R China

ISBN: (纸本)9783030050542;9783030050535

Mobile edge computing (MEC), as a prospective computing paradigm, can augment the computation capabilities of mobile devices through offloading the complex computational tasks from simple devices to MEC-enabled base station (BS) covering them. However, how to achieve optimal schedule remains a problem due to various practical challenges including imperfect estimation of channel state information (CSI), stochastic tasks arrivals and time-varying channel situation. By using Lyapunov optimization theory and Lagrange dual decomposition technique, we propose an optimal dynamic offloading and resource scheduling (oDors) approach to maximize a system utility balancing throughput and fairness under imperfect estimation of CSI. We derive the analytical bounds for the time-averaged data queues length and system throughput achieved by the proposed approach which depends on the channel estimation error. We show that without prior knowledge of tasks arrivals and wireless channels, oDors achieves a system capacity which can arbitrarily approach the optimal system throughput. Simulation results confirm the theoretical analysis on the performance of oDors.

关键词： Mobile edge computing Imperfect CSI Channel estimation Stochastic optimization

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Efficient Sparse Matrix-Vector Multiplication on GPUs using the CSR Storage Format 14

Efficient Sparse Matrix-Vector Multiplication on GPUs using ...

引用

International conference on High Performance computing, Networking, Storage and Analysis

作者： Greathouse, Joseph L. Daga, Mayank Adv Micro Devices Inc AMD Res Ft Collins CO 80528 USA

ISBN: (纸本)9781479955008

The performance of sparse matrix vector multiplication (SpMV) is important to computational scientists. Compressed sparse row (CSR) is the most frequently used format to store sparse matrices. However, CSR-based SpMV on graphics processing units (GPUs) has poor performance due to irregular memory access patterns, load imbalance, and reduced parallelism. This has led researchers to propose new storage formats. Unfortunately, dynamically transforming CSR into these formats has significant runtime and storage overheads. We propose a novel algorithm, CSR-Adaptive, which keeps the CSR format intact and maps well to GPUs. Our implementation addresses the aforementioned challenges by (i) efficiently accessing DRAM by streaming data into the local scratchpad memory and (ii) dynamically assigning different numbers of rows to each parallel GPU compute unit. CSR-Adaptive achieves an average speedup of 14.7x over existing CSR-based algorithms and 2.3x over clSpMV cocktail, which uses an assortment of matrix formats.

关键词： Sparse matrix-vector multiplication (SpMV) general purpose computation on graphics processing units (GPGPU) compressed sparse row (CSR) AMD performance acceleration

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Analytics-Aware Storage of Surveillance Videos: Implementation and Optimization 6

Analytics-Aware Storage of Surveillance Videos: Implementati...

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6th IEEE International conference on Smart computing (SMARTCOMP)

作者： Tsai, Min-Han Venkatasubramanian, Nalini Hsu, Cheng-Hsin Natl Tsing Hua Univ Hsinchu Taiwan Univ Calif Irvine Irvine CA USA

ISBN: (纸本)9781728169972

Increasingly more surveillance cameras in smart environments stream videos to storage servers for on-demand video analytics queries in the future. Unlike on-demand video services, in which maximizing the user-perceived video quality is the design objective, the considered storage servers aim to retain as much information as possible while offering enough space for incoming video clips. In this paper, we design, optimize, and implement an analytics-aware storage server on a smart campus testbed at NTHU, Taiwan, which consists of eight smart street lamps equipped with various sensors, network devices, analytics servers, and a storage server. We focus on the design and implementation of the storage server, and consider two key research problems: (i) how to efficiently determine the information amount of individual video clips and (ii) how to intelligently downsample individual video clips. More specifically, the first problem is to sample video frames from the stored video clips to analyze for approximations of the information amount without overloading the storage server. The resulting information amount is fed into the second problem to decide the video downsampling approaches for retaining as much information amount as possible without consuming excessive storage space. We propose two efficient algorithms to solve these two problems and compare their performance with the current practices via real experiments on our smart campus testbed. Our experiment results reveal the practicality and efficiency of our proposed design and algorithms, e.g., compared to the current practices, our storage server: (i) improves the per-request information amount by up to similar to 4 times, (ii) increases the total information amount by at most similar to 20%, (iii) boosts the number of saved video clips by up to similar to 35%, (iv) runs in real-time, and (v) scales well with larger storage space.

关键词： Video analysis and downsampling information quantization storage optimization edge computing

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Edge-Assisted Federated Learning: An Empirical Study from Software Decomposition Perspective 20th

Edge-Assisted Federated Learning: An Empirical Study from So...

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20th International conference on algorithms and Architectures for Parallel Processing (ICA3PP)

作者： Shi, Yimin Duan, Haihan Chi, Yuanfang Gai, Keke Cai, Wei Chinese Univ Hong Kong Shenzhen Peoples R China Shenzhen Inst Artificial Intelligence & Robot Soc Shenzhen Peoples R China Univ British Columbia Vancouver BC Canada Beijing Insitute Technol Beijing Peoples R China

ISBN: (纸本)9783030602390;9783030602383

Federated learning is considered to be a privacy-preserving collaborative machine learning training method. However, due to the general limitation of the computing ability of the terminal device, the training efficiency becomes an issue when training some complex deep neural network models. On the other hand, edges, the nearby stationary devices with higher computational capacity, might serve as a help. This paper presents the design of a component-based federated learning framework, which facilitates the offloading of training layers to nearby edge devices while preserving the users' privacy. We conduct an empirical study on a classic convolutional neural network to validate our framework. Experiments show that this method can effectively shorten the time cost for mobile terminals to perform local training in the federated learning process.

关键词： Federated learning Deep learning Mobile edge computing Program decomposition Distributed computing

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Slow-light vortices in periodic waveguides

Slow-light vortices in periodic waveguides

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conference on Advances in Slow and Fast Light ii

作者： Sukhorukov, Andrey A. Ha, Sangwoo Desyatnikov, Anton S. Lavrinenko, Andrei V. Kivshar, Yuri S. Australian Natl Univ Ctr Ultrahigh Bandwidth Devices Opt Syst CUDOS Res Sch Phys & Engn GPO Box 4 Canberra ACT 0200 Australia Australian Natl Univ Nonlinear Phys Ctr Res Sch Phys & Engn Canberra ACT 0200 Australia Tech Univ Denmark NanoDTU DTU Foton Dept Photon Engn DK-2800 Lyngby KGS Denmark

ISBN: (纸本)9780819474728

We reveal that the reduction of the group velocity of light in periodic waveguides is generically associated with the presence of vortex energy flows. We show that the energy flows are gradually frozen for slow-light at the Brillouin zone edge, whereas vortices persist for slow-light states having non-vanishing phase velocity inside the Brillouin zone. We also demonstrate that presence of vortices can be linked to the absence of slow-light at the zone edge, and present calculations illustrating these general results.

关键词： Slow-light photonic crystal vortex

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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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SymED: Adaptive and Online Symbolic Representation of Data on the Edge 29th

SymED: Adaptive and Online Symbolic Representation of Data o...

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29th International conference on Parallel and Distributed computing (Euro-Par)

作者： Hofstaetter, Daniel Ilager, Shashikant Lujic, Ivan Brandic, Ivona Vienna Univ Technol Vienna Austria Ericsson Nikola Tesla Zagreb Croatia

ISBN: (纸本)9783031396977;9783031396984

The edge computing paradigm helps handle the Internet of Things (IoT) generated data in proximity to its source. Challenges occur in transferring, storing, and processing this rapidly growing amount of data on resource-constrained edge devices. Symbolic Representation (SR) algorithms are promising solutions to reduce the data size by converting actual raw data into symbols. Also, they allow data analytics (e.g., anomaly detection and trend prediction) directly on symbols, benefiting large classes of edge applications. However, existing SR algorithms are centralized in design and work offline with batch data, which is infeasible for real-time cases. We propose SymED - Symbolic Edge Data representation method, i.e., an online, adaptive, and distributed approach for symbolic representation of data on edge. SymED is based on the Adaptive Brownian Bridge-based Aggregation (ABBA), where we assume low-powered IoT devices do initial data compression (senders) and the more robust edge devices do the symbolic conversion (receivers). We evaluate SymED by measuring compression performance, reconstruction accuracy through Dynamic Time Warping (DTW) distance, and computational latency. The results show that SymED is able to (i) reduce the raw data with an average compression rate of 9.5%;(ii) keep a low reconstruction error of 13.25 in the DTW space;(iii) simultaneously provide real-time adaptability for online streaming IoT data at typical latencies of 42ms per symbol, reducing the overall network traffic.

关键词： Internet of Things Edge computing Symbolic data representation Edge storage and analytics Data compression Time series

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Pervasive Evolutionary algorithms on Mobile devices

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10th International Work-conference on Artificial Neural Networks (IWANN 2009)

作者： Garcia-Sanchez, Pablo Sevilla, Juan P. Merelo, Juan J. Mora, Antonio M. Castillo, Pedro A. Laredo, Juan L. J. Casado, Francisco Univ Granada CITIC UGR Dept Comp Architecture & Comp Technol E-18071 Granada Spain

ISBN: (纸本)9783642024801

This paper presents a Java framework to implement distributed applications via Bluetooth. It provides a high-level Application Programming Interface (API) which simplifies the creation of applications for Bluetooth devices in Java ME and Java SE platforms. This, framework is based in a client-server architecture and an event-driven asynchronous communication mechanism. As an example Of Use, we solve two well-known evolutionary computation problems (the Traveler Salesman Problem and the Wave Function Problem).

关键词： Bluetooth

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CMOS-compatible plasmonic waveguide platform and ring resonator for nanoscale electronic-photonic integrated circuits

CMOS-compatible plasmonic waveguide platform and ring resona...

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Optical Fiber Communication conference, OFC 2012

作者： Bai, Ping Chu, Hong-Son Li, Er-Ping Zhu, Shiyang Lo, Patrick Guo-Qiang Electronics and Photonics Department Institute of High Performance Computing A STAR 1 Fusionopolis way #16-16 Connexis 138632 Singapore Institute of Microelectronics A STAR 11 Science Park road Science Park II 117685 Singapore

ISBN: (纸本)9781557529381

We present a novel plasmonic waveguide platform Cu/SiO2/Si/SiO2/Cu to build high-performance CMOS-compatible plasmonic devices. The platform is based on metal-silica-silicon hybrid plasmonic waveguides. The optical performance of the waveguide platform is investigated and analyzed extensively with various geometries both theoretically and experimentally. The propagation loss can be achieved as low as 0.4 dB/μm with a small cross section SiO2/Si/SiO2 of 100×340 nm2. As an example, a plasmonic ring resonator with the ring radius of 0.91 μm is designed with the platform. Excellent optical performance is measured including a free spectral range of 130 nm, extinction ratio of 24 dB, and insertion loss of 2.5 dB. We believe this work will be useful for the integration of nanoscale electronic-photonic integrated circuits. © 2012 OSA.

关键词： Silica

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