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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41-50 订阅

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Physical Aware Clustering Training Method for Integrated photonic Convolution Neural Network with Nonlinear Distributed Weights

Physical Aware Clustering Training Method for Integrated Pho...

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Asia Communications and photonics conference and Exhibition (ACP)

作者： Yue Jiang Wenjia Zhang Xuying Liu Wenyu Zhu Zuyuan He State Key Laboratory of Advanced Optical Communication Systems and Networks Shanghai Jiao Tong University Shanghai China

The computing power of the photonic Convolution Neural Network (CNN) have achieved Tera-level operations per second (OPS) for supporting machine vision algorithms. However, restrained by the nonlinear characteristics of weighting devices, it is hard to realize convergent training of photonic CNN under poor controlling precision. In this paper we proposed a Physical Aware Clustering training method where the Physical Aware Cluster Quantizer is embed with the straight through estimator (STE) algorithm for integrated photonic CNN with quantized and nonlinear distributed weights. Experiment shows that, by employing the upgraded STE methods (namely, STE plus), the photonic CNN using micro ring weighting bank and PAM4 controlling modules achieves the nearly 99.3% accuracy ratio for Fashion MNIST recognition task, whereas only 41.5% of that by using baseline STE training algorithms with the same physical devices.

关键词： Training Time-frequency analysis Quantization (signal) Convolution Neural networks Clustering algorithms Prototypes

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Determining Best Hardware, Software and Data Structures for Worker Guidance during a Complex Assembly Task 22

Determining Best Hardware, Software and Data Structures for ...

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15th ACM International conference on Pervasive Technologies Related to Assistive Environments (PETRA)

作者： Anzengruber-Tanase, Bernhard Sopidis, Georgios Haslgruebler-Huemer, Michael Ferscha, Alois Pro2Future GmbH Linz Austria JKU Linz Linz Austria

ISBN: (纸本)9781450396318

A widespread challenge in the industrial domain is the modernization and digitization of assembly processes involving human workers to increase production efficiency and thus stay competitive with rival companies. Specifically, in assembly processes involving low lot sizes, human workers are required to deal with variations to individual assembly work processes due to product customization. In case of complex tasks this leads to mistakes and further expensive dis- and reassembly steps. This paper investigates which are the quantifiably best data sources, pre-procession steps, features, and machine learning algorithms to determine the correct execution of a specific work process in the manufacturing environment. To answer this question, a wearable sensor system consisting of multiple heterogeneous sensor devices was developed. The data used for this work was specifically collected from the actual production environment in multiple recording sessions, and particular focus was given to achieve this in a realistic yet controlled way. An assistance provisioning pipeline for industrial workers consisting of (i) an activity recognition system, (ii) a work flow correlation engine, (iii) a wrench activity estimator and a (iv) feedback system was developed. These systems were designed and evaluated using authentic, task-specific expert knowledge and using a grid search study to determine the best selection of data sources, pre-procession steps, features, and machine learning algorithms. This study was able to answer the given research question and reifies the final results in the form of a guidance system to be deployed in an industrial manufacturing line.

关键词： assistance systems pervasive computing workflow tracking industrial manufacturing machine learning

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Low Power Consumption Online Testing Technique for Wearable Wireless Body Area Networks 37

Low Power Consumption Online Testing Technique for Wearable ...

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37th conference on Design of Circuits and Integrated Circuits (DCIS)

作者： Lasaga Arana, Inigo Mikel Ramirez Barcenas, Alberto Garcia-Valderas, Mario Lopez-Ongil, Celia Univ Carlos III Madrid Dept Elect Technol Leganes Spain

ISBN: (数字)9781665459501

ISBN: (纸本)9781665459501

Internet of Things (IoT) is a growing field of research and in real-life applications. Cyber-physical Systems are aimed to monitor physical variables, processing data, applying artificial intelligent algorithms, and responding in real time for further actions. For example, on-the-edge medical monitoring of chronic illnesses or supervising of an industrial process can help to early detect small anomalies or misfunctions, preventing catastrophic events or assuring minimum quality thresholds. These systems require continuous monitoring, data collection, signal processing and analysis, which demand smart sensors, efficient wireless communications, and powerful processing modules. This is especially critical in wearable body area networks (BAN) where size, weight and power consumption are key elements in the system's usability and attractiveness. Dependability becomes a big concern in the design and deployment of wearable devices that can be considered unsupervised wireless nodes, with high complexity in architecture and functionality. This work proposes the integration of a software online testing procedure for monitoring intra-node faults appearance, detecting the presence of errors, reporting their effects, and performing a graceful degradation when possible, maintaining a low power consumption. The online testing proposal has been implemented and validated in a real system, Bindi from the UC3M4Safety team. The effect on the power consumption has been measured showing a very low effect on the overall power budget.

关键词： IoT CPS W2BAN Online testing Dependability On-the-edge computing Power consumption Affective computing

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All-Optical computing for Super-Resolution Direction of Arrival Estimation

All-Optical Computing for Super-Resolution Direction of Arri...

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Asia Communications and photonics conference and Exhibition (ACP)

作者： Sheng Gao Hang Chen Haiou Zhang Zhi Sun Yuan Shen Xing Lin Department of Electronic Engineering Tsinghua University Beijing China

ISBN: (数字)9798350379266

ISBN: (纸本)9798350379273

Direction of arrival estimation technology is the foundation for communications, radar, navigation, etc. However, conventional electronic devices for DOA estimation require expensive RF circuits, high-precision analogue-to-digital converters and complex digital signal processing algorithms. Here, we propose a super-resolution diffractive neural network (S-DNN) to directly process electromagnetic (EM) waves for super-resolution DOA estimation over a broadband frequency range. The multilayer meta-structures of the S-DNN produces super-oscillatory angular responses in local angular range, which can perform all-optical DOA estimation with angular resolution beyond the diffraction limit. Space-time multiplexing of passive and reconfigurable S-DNNs is used to achieve high-resolution DOA estimation over a wide field of view. Experiments show that the angular resolution of the S-DNN is four times higher than the diffraction limited resolution. The fabricated S-DNN can be used for superresolution DOA estimation of multiple coherent sources over a 5 GHz frequency bandwidth, with an estimation delay that is in principle two to four orders of magnitude lower than commercial devices. We also use the all-optical DOA estimation capability of the S-DNN to provide angular direction to reconfigurable intelligent surface, to achieve low-latency and low-power integrated sensing and communication. Our work is an important step towards enabling various wireless sensing and communication tasks using photonic computing processors that outperform electronic computing in both computational paradigm and performance.

关键词： Wireless communication Radio frequency Wireless sensor networks Direction-of-arrival estimation Program processors Diffraction Superresolution Neural networks Estimation photonics

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photonic-aware Neural Networks for Packet Classification in beyond 5G Networks 13

Photonic-aware Neural Networks for Packet Classification in ...

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13th International conference on the Network of the Future, NoF 2022

作者： Paolini, Emilio Civerchia, Federico De Marinis, Lorenzo Valcarenghi, Luca Maggiani, Luca Andriolli, Nicola Scuola Superiore sant'Anna Pisa56124 Italy TeCIP Institute Departments of Excellence Robotics & AI Italy National Research Council of Italy CNR-IEIIT Pisa56122 Italy Sma-RTy Italiat SRL Carugate20061 Italy

ISBN: (数字)9781665472548

ISBN: (纸本)9781665472548

The benefits introduced by novel network technologies such as 5G and beyond, including low latency and support for billions of devices, have the potential to transform the lives of people. However, the features promised by these new technologies have also attracted malicious actors, with various motivations for attacking the network infrastructure, from cybercrime-based frauds to political goals. Thus, to enable the full potential of the emerging network technologies, it is necessary to take into accounts these attacks and develop tailored countermeasures. One future direction in mitigating the risks of potential attacks is the automatic classification of malicious packets, with the possibility to drop them if classified in the attack category. Hence, in this context, we propose a solution based on Neural Networks (NNs) to automatically classify packets into two classes, i.e., benign and attack, directly in the Radio Access Network (RAN), specifically inspecting packets when they are relayed at the next generation eNB (gNB)-Central Unit (CU) level. Since NNs can be computationally intensive algorithms, potentially increasing the latency of the network, we decide to leverage photonic-Aware Neural Network (PANN), photonic accelerators able to perform NN computations in the analog optical domain and with time-of-flight latency. We devised two different PANN architectures, considering different photonic constraints. The classification performance of the two architectures has been assessed on the CICIDS-2017 dataset and compared with electronic counterparts. Results proved that the F1-score loss due to underlying hardware constraints is negligible, paving the way for PANN applications in next generation networks. © 2022 IEEE.

关键词： 5G mobile communication systems

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Optimized Live 4K Video Multicast Streaming on Commodity WiGig devices

Optimized Live 4K Video Multicast Streaming on Commodity WiG...

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International conference on Distributed computing Systems

作者： Zhaoyuan He Changhan Ge Wangyang Li Lili Qiu Peijie Li Ghufran Baig The University of Texas at Austin Austin TX United States of America Microsoft Research Asia Shanghai People's Republic of China Google Austin TX United States of America

ISBN: (数字)9798350386059

ISBN: (纸本)9798350386066

The popularity of 4K videos is on the rise. However, streaming such high-quality videos over mm Wave to several users presents significant challenges due to directional communication, fluctuating channels, and high bandwidth demands. To address these challenges, this paper introduces an innovative 4K layered video multicast streaming system. We (i) develop a video quality model tailored for layered video coding, (ii) optimize resource allocation, scheduling, and beamforming based on the channel conditions of different users, and (iii) design a streaming strategy that integrates fountain code to eliminate redundancy in multicast groups, coupled with a Leaky-Bucket approach for congestion control. We implement our system on Commodity-Off- The-Shelf (COTS) WiGig devices and demonstrate its effectiveness through comprehensive testbed and emulation experiments.

关键词： Video coding Scheduling algorithms Source coding Redundancy Emulation Dynamic scheduling Quality assessment

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Reservoir Based Edge Training on RF Data To Deliver Intelligent and Efficient IoT Spectrum Sensors 7

Reservoir Based Edge Training on RF Data To Deliver Intellig...

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7th IEEE International conference on Smart computing (SMARTCOMP)

作者： Kokalj-Filipovic, Silvija Toliver, Paul Johnson, William Miller, Rob Peraton Labs Inc Basking Ridge NJ 07920 USA

ISBN: (纸本)9781665412520

Current radio frequency (RF) sensors at the Edge lack the computational resources to support practical, in-situ training for intelligent spectrum monitoring. This is true for sensor data classification in general. We propose a solution via Deep Delay Loop Reservoir computing (DLR), a processing architecture that supports general machine learning algorithms on compact mobile devices by leveraging delay-loop reservoir computing in combination with innovative electro-optical hardware. With both digital and photonic realizations of our design of the loops, DLR delivers reductions in form factor, hardware complexity and latency, compared to the State-of-the-Art (SoA). The main impact of the reservoir is to project the input data into a higher dimensional space of reservoir state vectors in order to linearly separate the input classes. Once the classes are well separated, traditionally complex, power-hungry classification models are no longer needed for the learning process. Yet, even with simple classifiers based on Ridge regression (RR), the complexity grows at least quadratically with the input size. Hence, the hardware reduction required for training on compact devices is in contradiction with the large dimension of state vectors. DLR employs a RR-based classifier to exceed the SoA accuracy, while further reducing power consumption by leveraging the architecture of parallel (split) loops. We present DLR architectures composed of multiple smaller loops whose state vectors are linearly combined to create a lower dimensional input into Ridge regression. We demonstrate the advantages of using DLR for two distinct applications: RF Specific Emitter Identification (SEI) for IoT authentication, and wireless protocol recognition for IoT situational awareness.

关键词： IoT reservoir computing edge computing in-situ training delay loops wireless security specific emitter identification RF fingerprinting RF protocol recognition

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Nanophotonics for Quantum Information Processing and Artificial Intelligence

Nanophotonics for Quantum Information Processing and Artific...

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2022 27th OptoElectronics and Communications conference (OECC) and 2022 International conference on photonics in Switching and computing (PSC)

作者： Nicholas C. Harris Lightmatter

ISBN: (数字)9784885523366

ISBN: (纸本)9781665486064

Compute requirements for state-of-the-art artificial intelligence applications are growing at a rate that is unprecedented in the field of high performance computing. New hardware technologies and architectures are being developed towards closing the gap between traditional compute advancement cycles, typically bound by an 18 month cadence, and what is required to power increasingly large parameter count neural networks. Silicon photonics is typically viewed as a communications platform. Here, we will discuss a brief history of optical computing in both the classical and quantum regimes, what's changed, and how silicon photonics can be applied to the problem of accelerating artificial intelligence algorithms. We will also discuss a novel, wafer-scale, switchable photonic fabric implemented in a hybrid CMOS photonics process with reticle-stitched transistors and photonic components and integrated iii-V lasers.

关键词： photonics Artificial intelligence Quantum computing Information processing Transistors Switching circuits Social networking (online)

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EQC: Ensembled Quantum computing for Variational Quantum algorithms 22

EQC: Ensembled Quantum Computing for Variational Quantum Alg...

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49th IEEE/ACM Annual International Symposium on Computer Architecture (ISCA)

作者： Stein, Samuel Wiebe, Nathan Ding, Yufei Bo, Peng Kowalski, Karol Baker, Nathan Ang, James Li, Ang Pacific Northwest Natl Lab Richland WA 99354 USA Univ Calif Santa Barbara Santa Barbara CA USA Univ Toronto Toronto ON Canada

ISBN: (纸本)9781450386104

Variational quantum algorithm (VQA), which is comprised of a classical optimizer and a parameterized quantum circuit, emerges as one of the most promising approaches for harvesting the power of quantum computers in the noisy intermediate scale quantum (NISQ) era. However, the deployment of VQAs on contemporary NISQ devices often faces considerable system and time-dependant noise and prohibitively slow training speeds. On the other hand, the expensive supporting resources and infrastructure make quantum computers extremely keen on high utilization. In this paper, we propose a virtualized way of building up a quantum backend for variational quantum algorithms: rather than relying on a single physical device which tends to introduce ever-changing device-specific noise with less reliable performance as time-since-calibration grows, we propose to constitute a quantum ensemble, which dynamically distributes quantum tasks asynchronously across a set of physical devices, and adjusts the ensemble configuration with respect to machine status. In addition to reduced machine-dependant noise, the ensemble can provide significant speedups for VQA training. With this idea, we build a novel VQA training framework called EQC - a distributed gradient-based processor-performance-aware optimization system - that comprises: (i) a system architecture for asynchronous parallel VQA cooperative training;(ii) an analytical model for assessing the quality of a circuit output concerning its architecture, transpilation, and runtime conditions;(iii) a weighting mechanism to adjust the quantum ensemble's computational contribution according to the systems' current performance. Evaluations comprising 500K times' circuit evaluations across 10 IBMQ NISQ devices using a VQE and a QAOA applications demonstrate that EQC can attain error rates very close to the most performant device of the ensemble, while boosting the training speed by 10.5x on average (up to 86x and at least 5.2x). EQC is available at

关键词： Quantum computing Variational Quantum algorithms Distributed computing

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Towards Interval Type-2 Fuzzy-Based PPG Quality Assessment for Physiological Monitoring

Towards Interval Type-2 Fuzzy-Based PPG Quality Assessment f...

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IEEE International conference on Fuzzy Systems (FUZZ-IEEE) / IEEE World Congress on Computational Intelligence (IEEE WCCI) / International Joint conference on Neural Networks (IJCNN) / IEEE Congress on Evolutionary Computation (IEEE CEC)

作者： Miranda, Jose A. Montoro, Alba Paez Lopez-Ongil, Celia Andreu-Perez, Javier Univ Carlos III Madrid Elect Technol Dept Leganes Spain Univ Essex Sch Comp Sci & Elect Engn Colchester Essex England

ISBN: (数字)9781665467100

ISBN: (纸本)9781665467100

Wearable technology is having a profound impact in healthcare applications. In this context, one of the main goals of current wearable systems is to provide new devices capable of delivering a greater amount of more precise information. Amongst the different applications, physiological continuous monitoring is becoming one of the most wanted features. In fact, most of the commercially and research grade wearable systems include cardiac activity acquisition, which is generally performed by means of photoplethysmography sensors (PPG). These optical-based sensors present different challenges related to the prevailing of a good signal quality. Thus, in case of dealing with digital processing algorithms which are responsible for extracting different features from such signal, this fact can lead to erroneous results. On this basis, this paper presents an ongoing work towards the design of an interval type-II fuzzy-based system for the PPG signal quality assessment. Moreover, this initial proposed system is implemented into a constrained 32-bit ARM Cortex-M4 system-on-chip. Specifically, the system uses a reduced set of features together with a low complexity fuzzy rule base Mamdani inference model, and is based on a non-overlapping 3-second signal processing window. Results show that the system achieved overall accuracy of 94.84%. The proposed system has great potential for integrating accurate and reliable continuous health monitoring systems into constrained edge devices.

关键词： Fuzzy Photoplethysmography Edge computing Signal Quality Assessment Matrix Profile Physiological Monitoring

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