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

共 299 条记录，以下是151-160 订阅

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1st International conference on Intelligent computing, Communication and devices, ICCD 2014

1st International Conference on Intelligent Computing, Commu...

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1st International conference on Intelligent computing, Communication and devices, ICCD 2014

ISBN: (纸本)9788132220114

The proceedings contain 88 papers. The special focus in this conference is on Intelligent computing, Communication and devices. The topics include: Neutrosophic logic applied to decision making;an online content syndication tool with built-in search and social sharing;future-generation network with internet of thing based on distributed cloud computing;genetic algorithm-based query expansion for improved information retrieval;genetic algorithm-based adaptive PID controller;a multi-objective cat swarm optimization algorithm for workflow scheduling in cloud computing environment;an improvement on NSGA-ii for finding fast the better optimal solution in multi-objective optimization problem;solving nonlinear constrained optimization problems using invasive weed optimization;preserving privacy in healthcare web services paradigm through Hippocratic databases;generation of array passwords using Petri net for effective network and information security;application of genetic algorithm for component optimization to deploy geoprocessing web;use of machine learning algorithms with SIEM for attack prediction;computational techniques for predicting cyber threats;an intelligent method to test feasibility predicate for robotic assembly sequence generation;a probabilistic method toward SLAM for mobile robotic systems;web service response time prediction using HMM and Bayesian network;enhancing security in cloud computing using Bi-directional DNA encryption algorithm;service composition using efficient multi-agents in cloud computing environment;criterions for databases in cloud computing environment;personalized web page recommendation using a graph-based approach to implicitly find influential users;ensuring data security and performance evaluation in cloud computing;financial trading system using combination of textual and numeric data;design and characterization of Ka-Band reflection-type IMPATT amplifier;on-chip improved single clock swing enhanced charge pump circuit;combining

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EdgeSP: Scalable Multi-device Parallel DNN Inference on Heterogeneous Edge Clusters 21st

EdgeSP: Scalable Multi-device Parallel DNN Inference on Hete...

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

作者： Gao, Zhipeng Sun, Shan Zhang, Yinghan Mo, Zijia Zhao, Chen Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China China Int Engn Consulting Corp Ltd Beijing Peoples R China

ISBN: (纸本)9783030953881;9783030953874

Edge computing has emerged as a promising line of research for processing large-scale data and providing low-latency services. Unfortunately, deploying deep neural networks (DNNs) on resource-limited edge devices presents unacceptable latency, hindering artificial intelligence from empowering edge devices. Prior solutions attempted to address this issue by offloading workload to the remote cloud. However, the cloud-assisted approach ignores that devices in the edge environment tend to exist as clusters. In this paper, we propose EdgeSP, a scalable multi-device parallel DNN inference framework that maximizes resource utilization of heterogeneous edge device clusters. We design a multiple fused-layer blocks parallelization strategy to reduce inter-device communication during parallel inference. Further, we add early exit branches to DNNs, empowering the device to trade-off latency and accuracy for a variety of sophisticated tasks. Experimental results show that EdgeSP enables inference latency acceleration of 2.3 x -3.7x for DNN inference tasks of various scales and outperforms the existing naive parallel inference method. Additionally, EdgeSP can provide high accuracy inference services under various latency requirements.

关键词： Edge computing Edge intelligence Parallel inference Deep neural networks Early-exit Internet of Things

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Enabling Machine Learning on Resource Constrained devices by Source Code Generation of the Learned Models 18th

Enabling Machine Learning on Resource Constrained Devices by...

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18th International conference on Computational Science (ICCS)

作者： Szydlo, Tomasz Sendorek, Joanna Brzoza-Woch, Robert AGH Univ Sci & Technol Dept Comp Sci Krakow Poland

ISBN: (纸本)9783319937014;9783319937007

Due to the development of IoT solutions, we can observe the constantly growing number of these devices in almost every aspect of our lives. The machine learning may improve increase their intelligence and smartness. Unfortunately, the highly regarded programming libraries consume to much resources to be ported to the embedded processors. Thus, in the paper the concept of source code generation of machine learning models is presented as well as the generation algorithms for commonly used machine learning methods. The concept has been proven in the use cases.

关键词： IoT Edge computing Machine learning

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On statistical multiplexing of variable-bit-rate video streams in mobile systems 09

On statistical multiplexing of variable-bit-rate video strea...

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17th ACM International conference on Multimedia, MM'09, with Co-located Workshops and Symposiums

作者： Hsu, Cheng-Hsin Hefeeda, Mohamed School of Computing Science Simon Fraser University Surrey BC Canada

ISBN: (纸本)9781605586083

We consider the problem of broadcasting multiple variable-bit-rate (VBR) video streams from a base station to many mobile devices over a wireless network, so that: (i) perceived quality on mobile devices is maximized, (ii) bandwidth utilization is maximized, and (iii) energy consumption of mobile devices is minimized. We show that this problem is NP-Complete. We propose an approximation algorithm for the base station to statistically multiplex and transmit multiple VBR streams to achieve these objectives. We analytically analyze the performance of our algorithm and prove that it achieves optimal bandwidth utilization and near-optimal energy saving. Our algorithm frees network operators from the manual and error-prone bandwidth reservation process, which is usually used in practice for broadcasting VBR streams. We implement the proposed algorithm in a trace-driven simulator, and conduct extensive simulations. The simulation results show that our algorithm outperforms the existing algorithms in many aspects, including number of late frames, number of concurrently broadcast video streams, and energy saving of mobile devices. We also implement the proposed algorithm in a real testbed for video broadcasting as a proof of concept. The results from the testbed confirm that the proposed algorithm: (i) does not result in playout glitches, (ii) achieves high energy saving, and (iii) runs in real time. Copyright 2009 ACM.

关键词： Energy conservation

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T2C: A Multi-User System for Deploying DNNs in a Thing-to-Cloud Continuum 18

T2C: A Multi-User System for Deploying DNNs in a Thing-to-Cl...

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18th IEEE International conference on Mobility, Sensing and Networking (MSN)

作者： Hsieh, Chia-Ying Venkateswaran, Praveen Venkatasubramanian, Nalini Hsu, Cheng-Hsin Natl Tsing Hua Univ Hsinchu Taiwan IBM Res Armonk NY USA Univ Calif Irvine Irvine CA USA

ISBN: (纸本)9781665464574

The importance of IoT analytics in smart deployments has resulted in an increased use of powerful Deep Neural Network (DNN) models to extract insights from the growing amount of IoT sensor data. Traditional approaches that entirely offload computation and model deployment to cloud servers have been shown to be inefficient due to network congestion and latency concerns. However, with the improved capabilities of IoT devices, it has now become possible to distribute and host DNNs across IoT devices, edge servers and the cloud. In this paper, we propose a multi-user system, called T2C, to dynamically choose, deploy, monitor and control DNN-driven IoT analytics in a thing-to-cloud continuum. T2C leverages strategies such as multi-task learning, hitchhiking, early exit, and dynamic reconfiguration, to maximize the number of served user requests while simultaneously satisfying accuracy and latency requirements. We propose a suite of deployment planning and reconfiguration algorithms to dynamically deploy and migrate DNN layers between IoT devices, edge servers, and the cloud. We implement T2C in a prototype testbed and show that our system: (i) achieves 6.8X throughput boost compared to baseline algorithms in the planning phase, and (ii) improves the satisfied ratio by up to 35% in the operation and reconfiguration phase.

关键词： multi-task learning early exit edge computing distributed deep learning

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Silicon nanophotonic networks-on-chip for performance-energy optimized off-chip memory access

Silicon nanophotonic networks-on-chip for performance-energy...

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photonics in Switching, PS 2010

作者： Bergman, Keren Lightwave Research Lab Department of Electrical Engineering Columbia University New York NY United States

ISBN: (纸本)9781557528957

As chip multiprocessors (CMPs) scale to increasing numbers of cores and greater on-chip computational power, the gap between the available off-chip bandwidth and that which is required to appropriately feed the processors continues to widen under current memory access architectures. For many high-performance computing applications, the bandwidth available for both on- and offchip communications can play a vital role in efficient execution due to the use of data-parallel or data-centric algorithms. Electronic interconnected systems are increasingly bound by their communications infrastructure and the associated power dissipation of high-bandwidth data movement.

关键词： All optical devices Circuit switched networks Optical networks Optical signals Optical transceivers Signal regeneration

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Exploiting Frame Similarity for Efficient Inference on Edge devices 42

Exploiting Frame Similarity for Efficient Inference on Edge ...

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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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Strong Modulation of Absorption and Third-Harmonic Generation in Resonant Metasurfaces based on VO₂ 9th

Strong Modulation of Absorption and Third-Harmonic Generatio...

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9th International conference on photonics, Optics and Laser Technology (PHOTOPTICS)

作者： Marni, Margherita de Ceglia, Domenico Univ Padua Dept Informat Engn Padua Italy

ISBN: (纸本)9789897584923

Control of linear and nonlinear optical signals is of key importance in a variety of applications, including signal processing, optical computing and energy harvesting, to name just a few. Optical modulation and switching, and more generally tunability in photonic devices, are usually achieved in the visible and near-infrared range by carrier injection, chemical or mechanical activation, or by deploying materials with large electro-optic or optical nonlinear coefficients. However, these mechanisms are inherently weak and therefore require intense control signals in order to produce significant modulation effects. Here we adopt a nanophotonic solution in which a resonant film of a volatile phase-change material, vanadium dioxide, is inserted between an array of antennas and a metallic backplane. Our design takes advantage of (i) the large refractive-index change of VO2 at its insulator-to-metal transition and (ii) the field enhancements available when the Fabry-Perot resonance of the film and the plasmonic resonance of the antennas are exited. In response to the VO2 phase transition, not only does our metasurface provide a strong and broadband modulation of linear absorption and reflection but it also shows a drastic variation of third-harmonic generation, with a conversion-efficiency contrast higher than three orders of magnitude.

关键词： Metasurfaces Nonlinear Optics Harmonic Generation Nanoantennas Tunable devices Nanophotonics Phase-change Materials

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Development of a Sleep Monitoring System by Using a Depth Sensor: A Pilot Study

Development of a Sleep Monitoring System by Using a Depth Se...

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10th International conference on Applied Human Factors and Ergonomics (AHFE) / International conference on Human Factors and Ergonomics in Healthcare and Medical devices

作者： Park, Jangwoon Kim, Jungyoon Park, Jaehyun Sheta, Alaa Murphey, Christina Um, Dugan Texas A&M Univ Corpus Christi Dept Engn Corpus Christi TX 78412 USA Kent State Univ Dept Comp Sci Kent OH 44242 USA Incheon Natl Univ Dept Ind & Management Engn Incheon South Korea Texas A&M Univ Corpus Christi Dept Comp Sci Corpus Christi TX USA Texas A&M Univ Corpus Christi Coll Nursing & Hlth Sci Corpus Christi TX USA

ISBN: (纸本)9783030204518;9783030204501

Sleep is an essential part of health and longevity persons. As people grow older, the quality of their sleep becomes vital. Poor sleep quality can make negative physiological, psychological, and social impacts on the elderly population, causing a range of health problems including coronary heart disease, depression, anxiety, and loneliness. Early detection, proper diagnosis, and treatments for sleep disorders can be achieved by identifying sleep patterns through long-term sleep monitoring. Although many studies developed sleep monitoring systems by using non-invasive measures such as body temperature, pressure, or body movement signal, research is still limited to detect sleep position changes by using a depth camera. The present study is intended (1) to identify concerns on the existing sleep monitoring system based on the literature review and (2) propose to developing a non-invasive sleep monitoring system using an infrared depth camera. For the literature review, various journal/conference papers have been reviewed to understand the characteristics, tools, and algorithms of the existing sleep monitoring systems. For the system development and validation, we collected data for the sleep positions from two subjects (35 years old man and 84 years old women) during the four-hour sleep. Kinect ii depth sensor was used for data collection. We found that the averaged depth data is useful measure to notify the participants' positional changes during the sleep.

关键词： Sleep quality Sleepmovement Sleep pattern Kinect ii depth sensor

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Optical Implementation of Neural Learning algorithms Based on Cross-gain Modulation in a Semiconductor Optical Amplifier 6

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conference on Optoelectronic devices and Integration VI

作者： Li, Qiang Wang, Zhi Le, Yansi Sun, Chonghui Song, Xiaojia Wu, Chongqing Beijing Jiaotong Univ Sch Sci Inst Opt Informat Beijing 100044 Peoples R China Minist Educ Key Lab Luminescence & Opt Informat Beijing 100044 Peoples R China

ISBN: (数字)9781510604582

ISBN: (纸本)9781510604575;9781510604582

Neuromorphic engineering has a wide range of applications in the fields of machine learning, pattern recognition, adaptive control, etc. photonics, characterized by its high speed, wide bandwidth, low power consumption and massive parallelism, is an ideal way to realize ultrafast spiking neural networks (SNNs). Synaptic plasticity is believed to be critical for learning, memory and development in neural circuits. Experimental results have shown that changes of synapse are highly dependent on the relative timing of pre- and postsynaptic spikes. Synaptic plasticity in which presynaptic spikes preceding postsynaptic spikes results in strengthening, while the opposite timing results in weakening is called antisymmetric spike-timing-dependent plasticity (STDP) learning rule. And synaptic plasticity has the opposite effect under the same conditions is called antisymmetric anti-STDP learning rule. We proposed and experimentally demonstrated an optical implementation of neural learning algorithms, which can achieve both of antisymmetric STDP and anti-STDP learning rule, based on the cross-gain modulation (XGM) within a single semiconductor optical amplifier (SOA). The weight and height of the potentitation and depression window can be controlled by adjusting the injection current of the SOA, to mimic the biological antisymmetric STDP and anti-STDP learning rule more realistically. As the injection current increases, the width of depression and potentitation window decreases and height increases, due to the decreasing of recovery time and increasing of gain under a stronger injection current. Based on the demonstrated optical STDP circuit, ultrafast learning in optical SNNs can be realized.

关键词： Spike-timing-dependent plasticity photonic Neural Network Hebbian Learning Rule Cross-gain Modulation Neural network hardware Optical signal processing Optical computing

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