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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161-170 订阅

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PCM Enabled Low-Power photonic Accelerator for Inference and Training on Edge devices

PCM Enabled Low-Power Photonic Accelerator for Inference and...

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1st International conference on Smart Energy Systems and Artificial Intelligence (SESAI)

作者： Curry, Juliana Louri, Ahmed Karanth, Avinash Bunescu, Razvan George Washington Univ Dept Elect & Comp Engn Washington DC 20052 USA Ohio Univ Sch Elect Engn & Comp Sci Athens OH 45701 USA Univ N Carolina Dept Comp Sci Charlotte NC USA

ISBN: (纸本)9798350364613;9798350364606

The convergence of edge computing and artificial intelligence requires that inference is performed on-device to provide rapid response with low latency and high accuracy without transferring large amounts of data to the cloud. However, power and size limitations make it challenging for electrical accelerators to support both inference and training for large neural network models. To this end, we propose Trident, a low-power photonic accelerator that combines the benefits of phase change material (PCM) and photonics to implement both inference and training in one unified architecture. Emerging silicon photonics has the potential to exploit the parallelism of neural network models, reduce power consumption and provide high bandwidth density via wavelength division multiplexing, making photonics an ideal candidate for on-device training and inference. As PCM is recoilfigurable and non-volatile, we utilize it for two distinct purposes: (i) to maintain resonant wavelength without expensive electrical or thermal heaters, and (ii) to implement non-linear activation function, which eliminates the need to move data between memory and compute units. This multi-purpose use of PCM is shown to lead to significant reduction in energy consumption and execution time. Compared to photonic accelerators DEAP-CNN, CrossLight, and PIXEL, Trident improves energy efficiency by up to 43% and latency by up to 150% on average. Compared to electronic edge AI accelerators Google Coral which utilizes the Google Edge TPU and Bearkey TB96-AL Trident improves energy efficiency by 11% and 93% respectively. While NVIDIA AGX Xavier is more energy efficient, the reduced data movement and GST activation of Trident reduce latency by 107% on average compared to the NVIDIA accelerator. When compared to the Google Coral and the Bearkey TB96-AI, Trident reduces latency by 1413% and 595% on average.

关键词： neural networks photonic accelerators phase change material training inference

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Presentation Attack Detection for Mobile Device-Based Iris Recognition

Presentation Attack Detection for Mobile Device-Based Iris R...

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11th International Image Processing and Communications conference (IP and C)

作者： Bartuzi, Ewelina Trokielewicz, Mateusz Res & Acad Comp Network NASK Kolska 12 PL-01045 Warsaw Poland

ISBN: (纸本)9783030312541;9783030312534

Apart from ensuring high recognition accuracy, one of the main challenges associated with mobile iris recognition is reliable Presentation Attack Detection (PAD). This paper proposes a method of detecting presentation attacks when the iris image is collected in visible light using mobile devices. We extended the existing database of 909 bonafide iris images acquired with a mobile phone by collecting additional 900 images of irises presented on a color screen. We explore different image channels in both RGB and HSV color spaces, deep learning-based and geometric model-based image segmentation, and use Local Binary Patterns (LBP) along with the selected statistical images features classified by the Support Vector Machine to propose an iris PAD algorithm suitable for mobile iris recognition setups. We found that the red channel in the RGB color space offers the best-quality input samples from the PAD point of view. In subject-disjoint experiments, this method was able to detect 99.78% of screen presentations, and did not reject any live sample.

关键词： Biometrics Iris recognition Presentation Attack Detection Mobile devices

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Evolutionary algorithms for the calculation of electron distributions in Si-MOSFETS 4th

Evolutionary algorithms for the calculation of electron dist...

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International conference on Evolutionary Computation - 4th International conference on Parallel Problem Solving from Nature, PPSN 1996

作者： Jakumeit, J. II. Physikalisches Institut Universität zu Köln Zülpicher Str. 77 KölnD-50937 Germany

ISBN: (纸本)354061723X

The prediction of electron distributions in semiconductor devices is compulsory for the design of modern computer chips. In spite of increasing computation facilities the calculation of electron distributions at high energies is difficult resulting in significant deviations between theoretical predictions and measurement results. With evolutionary algorithms, however, it is possible to search for electron distributions which fit to experimental results and in this way backward calculate the distribution from measurements. Hereby the possibility to support the optimization of the evolutionary algorithm by a physical mutation operator which modifies individuals due to physical rules is important. This new approach was applied to the calculation of electron distributions and gate currents in Si-MOSFETS. The results demonstrate that the combination of physical model and evolutionary optimization is an interesting tool to compare theoretical models with experimental results. © 1996, Springer-Verlag. All rights reserved.

关键词： Evolutionary algorithms

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Two-qubit operation of two atoms embedded in photonic crystals for optical quantum computing

Two-qubit operation of two atoms embedded in photonic crysta...

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International conference on Transparent Optical Networks

作者： H. Nihei A. Okamoto Polytechnic College Aomori Japan Graduate School of Engineering Hokkaido University Sapporo Hokkaido Japan

We demonstrate the two-qubit operation of two atoms embedded in photonic crystals, which is realized by applying a small DC field to the atoms according to quantum algorithms.

关键词： Atom optics Optical computing Quantum computing photonic crystals Embedded computing Optical crosstalk Quantum entanglement Optical devices photonic band gap Educational institutions

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Service-Aware Cooperative Task Offloading and Scheduling in Multi-access Edge computing Empowered IoT 1

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23rd International conference on algorithms and Architectures for Parallel Processing (ICA3PP)

作者： Chen, Zhiyan Tao, Ming Li, Xueqiang He, Ligang Dongguan Univ Technol Sch Comp Sci & Technol Dongguan Peoples R China Univ Warwick Dept Comp Sci Coventry W Midlands England

ISBN: (数字)9789819708017

ISBN: (纸本)9789819708000;9789819708017

Multi-access edge computing(MEC) enables computation task offloading and data processing at close proximity to provide rich endusers services with ultra-low latency in Internet of things(IoT). However, the high heterogeneity of the edge node configuration and the diversity of services pose challenges in fully utilizing the computing capacity in MEC. In this paper, we consider the problem of service-aware cooperative task offloading and scheduling in a three-tier MEC empowered IoT where the service requests from IoT devices can be distributed among edge nodes or further offloaded to remote cloud. As this problem is proven to be NP-hard, we proposed a two-layer Cooperative workload Initialization and Distribution Algorithm (CIDA) to solve the problem with low time complexity by decomposing it into two subproblems: 1) the optimization problem of offloading profile under dynamic resource allocation determined by the workload type, and 2) optimization problem of computation resources allocation under given offloading profile. Extensive experiments demonstrate that CIDA achieves superior performance compared to other approaches and scales well as the system size increases.

关键词： Multi-access edge computing Services-aware offloading Task scheduling Resource allocation

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Quasi-random Approach in the Grid Application SALUTE

Quasi-random Approach in the Grid Application SALUTE

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8th International conference, PPAM 2009

作者： Atanassov, Emanouil Karaivanova, Aneta Gurov, Todor Bulgarian Acad Sci Inst Parallel Proc BU-1113 Sofia Bulgaria

ISBN: (纸本)9783642144028

Stochastic algorithms for Ultra-fast Transport in sEmiconductors (SALUTE) is a Grid Application which integrates a set of novel Monte Gallo, quasi-Monte Carlo and hybrid algorithms for solving various computationally intensive problems important for industry (design of modern semiconductor devices) SALUTE studies memory and quantum effects during the femtosecond relaxation process due to electron-phonon interaction in one-band semiconductors or quantum wires There are two main reasons for running this application on the Grid (i) quantum problems are very computationally intensive (ii) the inherently parallel nature of Monte Carlo applications makes efficient use of Grid resources I n this paper we study the quasirandom approach in SALUTE, using the scrambled Halton, Sobol and Niederreiter sequences A large number of tests have been performed on the SEEGRID grid infrastructure using specially developed grid implementation scheme Novel results for energy and density distribution, obtained in the inhomogeneous case with applied electric field are presented

关键词： Grid computing Monte Carlo methods Quasi-Monte Carlo Scrambled Halton Sobol and Niederrerter sequences Ultra-fast carrier transport

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Mobile Edge computing Network Control: Tradeoff Between Delay and Cost

Mobile Edge Computing Network Control: Tradeoff Between Dela...

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IEEE Global Communications conference (GLOBECOM) on Advanced Technology for 5G Plus

作者： Cai, Yang Llorca, Jaime Tulino, Antonia M. Molisch, Andreas F. Univ Southern Calif Los Angeles CA 90089 USA NYU 550 1St Ave New York NY 10012 USA Univ Napoli Federico II I-80138 Naples Italy

ISBN: (纸本)9781728182988

As mobile edge computing (MEC) finds widespread use for relieving the computational burden of compute- and interaction-intensive applications on end user devices, understanding the resulting delay and cost performance is drawing significant attention. While most existing works focus on single-task offloading in single-hop MEC networks, next generation applications (e.g., industrial automation, augmented/virtual reality) require advance models and algorithms for dynamic configuration of multi-task services over multi-hop MEC networks. In this work, we leverage recent advances in dynamic cloud network control to provide a comprehensive study of the performance of multi-hop MEC networks, addressing the key problems of multi-task offloading, timely packet scheduling, and joint computation and communication resource allocation. We present a fully distributed algorithm based on Lyapunov control theory that achieves throughput-optimal performance with delay and cost guarantees. Simulation results validate our theoretical analysis and provide insightful guidelines on the interplay between communication and computation resources in MEC networks.

关键词： Mobile edge computing delay-cost tradeoff Lyapunov optimization theory

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Smart Wearable devices for Prediction Using Machine Learning Techniques: Depth Exploration 7

Smart Wearable Devices for Prediction Using Machine Learning...

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7th International conference on Circuit Power and computing Technologies, ICCPCT 2024

作者： Mary, L. William Antony Raj, S. Albert SRM Institute of Science and Technology Department of Computer Applications India

ISBN: (纸本)9798350372816

The emergence of the COVID-19 epidemic has emphasized the continuous need for innovative and discreet methods to monitor and evaluate the progression of the disease. Wearable technology, equipped with an array of sensors and powered by advanced machine learning algorithms, presents a highly promising solution. These devices make use of sensors like heart rate monitors, pulse oximeters, temperature sensors, and accelerometers to continuously gather real-time data. This research explores how wearables, including fitness trackers and smartwatches, can be used to continuously monitor the COVID-19 disease and capture the data. It also explores applications in assessing COVID-19 severity through the utilization of machine learning techniques. Machine learning techniques including decision trees, support vector machines, and deep neural networks are used to process this data. This includes the early detection of warning signs, evaluating disease progression, and distinguishing between mild, moderate, and severe cases. The integration of patient-reported symptoms with wearable data further enhances the precision of assessing COVID-19 severity. © 2024 IEEE.

关键词： COVID-19

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A Fast, Secure, Reliable, and Resilient Data Transfer Framework for Pervasive IoT Applications

A Fast, Secure, Reliable, and Resilient Data Transfer Framew...

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37th IEEE conference on Computer Communications (IEEE INFOCOM)

作者： Montella, Raffaele Ruggieri, Mario Kosta, Sokol Univ Naples Parthenope Dept Sci & Technol Naples Italy Aalborg Univ Dept Elect Syst CMI Copenhagen Denmark

ISBN: (纸本)9781538659793

The number of online devices with on board sensors is growing steadily in the last years, leading to many solutions depending on data crowd-sourcing. In these scenarios, usually the collected data need to be transferred on a cloud infrastructure, where can later be aggregated and analyzed using specialized data processing algorithms. The simple task of data transfer becomes a problem in stressed environments such as space communication systems, drone monitoring or marine ecosystems, where the network connectivity is unstable and not available for long period of times. In this paper, we propose a novel Internet of Things (IoT) data transfer framework for cloud-based applications in delay tolerant environments. The framework implements a protocol that is i) elastic, taking advantage of short connection times;ii) lightweight, which makes it suitable for IoT devices;and iii) secure, which makes it a good choice also for applications with sensitive data. We evaluate the framework in a real system with a marine navigation application, demonstrating its benefits in terms of performance, resiliency, and security.

关键词： Mobile Cloud computing Internet of Things Delay Tolerant Networks Localization Location-based Services

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A GPU Accelerated Hyperspectral 3D Convolutional Neural Network Classification at the Edge with Principal Component Analysis Preprocessing 14th

A GPU Accelerated Hyperspectral 3D Convolutional Neural Netw...

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14th International conference on Parallel Processing and Applied Mathematics, PPAM 2022

作者： De Lucia, Gianluca Lapegna, Marco Romano, Diego CNR Naples80131 Italy Department of Mathematics and Applications University of Naples Federico II Naples80126 Italy

ISBN: (纸本)9783031304446

The Edge computing paradigm promises to transfer decision-making processes based on artificial intelligence algorithms to the edge of the network without the need to query servers far from the data collection point. Hyperspectral image classification is one of the application fields that can benefit most from the close relationship between Edge computing and Artificial Intelligence. It consists of a framework of techniques and methodologies for collecting and processing images related to objects or scenes on the Earth’s surface, employing cameras or other sensors mounted on Unmanned Aerial Vehicles. However, the computing performance of the edge devices is not comparable with those of high-end servers, so specific approaches are required to consider the influence of the computing environment on the algorithm development methodology. In the present work, we propose a hybrid technique to make the Hyperspectral Image classification through Convolutional Neural Network affordable on low-power and high-performance sensor devices. We first use the Principal Component Analysis to filter insignificant wavelengths to reduce the dataset dimension;then, we use a process acceleration strategy to improve the performance by introducing a GPU-based form of parallelism. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Principal component analysis

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