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检索条件"任意字段=IEEE International Conference on Embedded and Real-Time Computing Systems and Applications"

共 36894 条记录，以下是431-440 订阅

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real-time based Industrial Cable and Battery Testing using embedded Technology

Real-Time based Industrial Cable and Battery Testing using E...

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2023 international conference on Sustainable computing and Smart systems, ICSCSS 2023

作者： Kishor, A.G. Gopalakrishnan, N. Gowtham, J. Babu, P. K.S.Rangasamy College of Technology Department of Ece Tamilnadu India

ISBN: (纸本)9798350333602

Now a days in the automotive industry there are lot of issues in production sectors. The topics of cables and its testing are covered in it. During production time there are more issues such as the need of man power to check the cables, so the cable testing equipment is designed to increase the production. This innovative approach is used for reducing the time consumption of testing the cables and battery voltages for increasing the production volumes in automotive industries with the help of embedded technology. In this study an embedded technology is used for designing a Cable Testing Machine, which contains core wire separate line testing function available for special 6-pin shell connector and USB to round pin and USB to 2-pin and with the help of micro controller it is able to detect the open circuit and short circuits. © 2023 ieee.

关键词： Cables

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Experimental comparison between SBC and FPGA for embedded neural network acceleration

Experimental comparison between SBC and FPGA for embedded ne...

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ieee international conference on Communications (ieee ICC)

作者： Tamburello, Marialaura Caruso, Giuseppe Adami, Davide Giordano, Stefano Univ Pisa Dept Informat Engn Pisa Italy Univ Pisa CNIT Pisa Italy

ISBN: (纸本)9781538674628

Recent impressive growth of AI applications in the most diversified heterogeneous domains is largely motivated by the availability of hardware accelerators used from the backstage of data centers (such as TPU, Tensor Processing Units, or VPUs, Visual Processing Units) to the far edge of embedded devices equipped with DPUs and Deep Learning Processing Units. High level toolchains for a more friendly usability of these platform had similar relevance in the process. In this paper we considered edge devices that provide an essential contribution for the deployment of "distributed intelligence" and are used typically at the gateway, CPE or Edge computing level. One of the typical assumptions is that Field Programmable Gate Array (FPGA) are far more expensive - with respect to power consumption - than legacy SBCs (single board computers). The main contribution of the paper is a fair comparison (at the same clock frequency and with the same main CPU) of processing time and power consumption of two different boards used for deep neural network classification. We will highlight the relevance of classification speed with respect to common KPIs adopted to compare the performances of automatic classification such as Loss, Precision, Recall, etc. This will be particularly relevant in the challenging domains of hardware accelerated real time control loops to provide distributed intelligence at the application level but also at the inner functions of emerging networking architectures.

关键词： Artificial Intelligence (AI) classification Convolutional Neural Network (CNN) power consumption Deep Learning Processing Unit (DPU)

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24th international conference on embedded Computer systems: Architectures, Modeling, and Simulation, SAMOS 2024

24th International Conference on Embedded Computer Systems: ...

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24th international conference on embedded Computer systems: Architectures, Modeling, and Simulation, SAMOS 2024

ISBN: (纸本)9783031783760

The proceedings contain 38 papers. The special focus in this conference is on embedded Computer systems: Architectures, Modeling, and Simulation. The topics include: QCEDA: Using Quantum Computers for EDA;real-time Linux on RISC-V: Long-Term Performance Analysis of PREEMPT_RT Patches;RV-VP2: Unlocking the Potential of RISC-V Packed-SIMD for embedded Processing;A Novel System Simulation Framework for HBM2 FPGA Platforms;ONNX-To-Hardware Design Flow for Adaptive Neural-Network Inference on FPGAs;efficient Post-training Augmentation for Adaptive Inference in Heterogeneous and Distributed IoT Environments;pooling On-the-Go for NoC-Based Convolutional Neural Network Accelerator;Vitamin-V: Serverless Cloud computing Porting on RISC-V;Design and Implementation of an Open Source OpenGL SC 2.0.1 Installable Client Driver and Offline Compiler;Plan Your Defense: A Comparative Analysis of Leakage Detection Methods on RISC-V Cores;iVault: Architectural Code Concealing Techniques to Protect Cryptographic Keys;I2DS: FPGA-Based Deep Learning Industrial Intrusion Detection System;ACRA: A Cutting-Edge Analytics Platform for Advanced real-time Corruption Risk Assessment and Investigation Prioritization;post Quantum Cryptography Research Lines in the Italian Center for Security and Rights in Cyberspace;advancing Future 5G/B5G systems: The Int5Gent Approach;RISC-V Accelerators, Enablement and applications for Automotive and Smart Home in the ISOLDE Project;PMDI: An AI-Enabled Ecosystem for Cooperative Urban Mobility;Open Source Software Randomisation Framework for Probabilistic WCET Prediction on Multicore CPUs, GPUs and Accelerators;a Hypervisor Based Platform for the Development and Verification of Reliable Software applications.

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24th international conference on embedded Computer systems: Architectures, Modeling, and Simulation, SAMOS 2024

24th International Conference on Embedded Computer Systems: ...

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24th international conference on embedded Computer systems: Architectures, Modeling, and Simulation, SAMOS 2024

ISBN: (纸本)9783031783791

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Reinforcement Learning for Minimizing Communication Delay in Edge computing 42

Reinforcement Learning for Minimizing Communication Delay in...

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42nd ieee international conference on Distributed computing systems (ICDCS)

作者： Rajashekar, Kolichala IIT Bhilai Sejbahar India

ISBN: (数字)9781665471770

ISBN: (纸本)9781665471770

For real-time edge computing applications working under stringent deadlines, communication delay between IoT devices and edge devices needs to be minimized. In order to minimize the communication delay between the IoT devices and the edge devices, we need a sophisticated approach for assignment IoT devices to the edge devices. Most of the heuristics solutions previously used to tackle the problem faced issues being solution stuck at local optima and high computational over head. To that end, researchers used reinforcement learning (RL) algorithms to explore the search space to get near optimal solutions. For our work, we consider RL based algorithms and show the preliminary results.

关键词： Reinforcement learning real-time systems Delays Space exploration Internet of Things Distributed computing Edge computing

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Dragonfly Algorithm and Variants for Feature Selection: A Review

Dragonfly Algorithm and Variants for Feature Selection: A Re...

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2023 ieee international conference on Quantum Technologies, Communications, computing, Hardware and embedded systems Security, iQ-CCHESS 2023

作者： Prinson, P.T. Geetha, Angelina Hindustan Institute of Technology and Science Department of Computer Applications Chennai India Hindustan Institute of Technology and Science Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350314946

Ultimately, the key to successfully analysing real-world problems is to be diligent and thorough. It is important to carefully consider all available data, remove any redundant or unnecessary information, and approach the analysis with an open mind. By doing so, it is possible to gain valuable insights and make informed decisions that can help solve complex real-world problems. By removing redundant, irrelevant, and noisy data, feature selection (FS), a difficult machine learning problem, attempts to lessen the number of features while maintaining a decent degree of classification accuracy. Feature selection is intended to reduce features. This immediately reduces the search space and aids machine learning algorithms in using only the most significant features. A review of Dragonfly algorithms and variants is presented in this paper, emphasizing their main characteristics. Eventually, the conclusion concentrates on the present research on Dragonfly Algorithm, emphasizing its limitations with suggested solutions that lead to future directions © 2023 ieee.

关键词： Swarm intelligence

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DFT Static Verification using Early RTL Exploration and Debug for Mobile SoC and Edge AI applications 37

DFT Static Verification using Early RTL Exploration and Debu...

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37th international conference on VLSI Design (VLSID) / 23rd international conference on embedded systems (ES)

作者： Viswanath, Vinod Chakraborty, Kanad Real Intent Inc Sunnyvale CA 94089 USA

ISBN: (纸本)9798350384406

Functional verification problems such as clock domain crossing ( CDC), reset domain crossing (RDC), X-propagation and design for testability (DFT) readiness have been the mainstay of hardware verification flow for some time now. With the ever-increasing chip complexity for modern-day mobile, multicore systems-on-chip (SoCs) with built-in advanced image processing, and scalable AI microcontrollers with billions of gates and numerous clock domains with ultralow device geometries, these problems are not only more significant nowadays, but need to be addressed very early in the design process for a timely tape out and first-pass Silicon success;failing which, there will be prohibitively expensive design bug fixes and costly design iterations. Static methods that perform search and analysis techniques to check for failures under all possible test modes, scenarios, and cases, have emerged as a very promising paradigm for solving these problems early, during RTL design. This paper describes a novel static verification, debug, and sign-off tool, Meridian DFT, that can detect specific design issues affecting testability at early RTL in presence of one or more test modes and provide ways to debug and quantify such issues. We present results on large scale (100+ million gates) industrial designs from mobile SoC and AI/Edge domain chips.

关键词： Static Sign-off design-for-test (DFT) fault coverage early RTL exploration

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Topology Aware Cluster Configuration for Minimizing Communication Delay in Edge computing 42

Topology Aware Cluster Configuration for Minimizing Communic...

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42nd ieee international conference on Distributed computing systems (ICDCS)

作者： Rajashekar, Kolichala Paul, Souradyuti Karmakar, Sushanta Sidhanta, Subhajit IIT Bhilai Sejbahar India IIT Guwahati Gauhati India

ISBN: (数字)9781665471770

ISBN: (纸本)9781665471770

For real-time edge computing applications working under stringent deadlines, communication delay between IoT devices and edge devices needs to be minimized. Since the generalized assignment problem being NP-Hard, an optimal assignment of IoT devices to the edge cluster is hard. We propose the application RL based heuristics to obtain a near-optimal assignment of IoT devices to the edge cluster while ensuring that none of the edge devices are overloaded. We demonstrate that our algorithm outperforms the state-of-the-art.

关键词： Clustering algorithms real-time systems Delays Topology Internet of Things Distributed computing Edge computing

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LiST: An All-Linear-Layer Spatial-Temporal Feature Extractor with Uncertainty Estimation for RUL Prediction 19

LiST: An All-Linear-Layer Spatial-Temporal Feature Extractor...

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ieee 19th conference on Industrial Electronics and applications (ICIEA)

作者： Huang, Zhixin Gruhl, Christian Sick, Bernhard Univ Kassel Intelligent Embedded Syst Kassel Germany

ISBN: (纸本)9798350360875;9798350360868

In the context of Remaining Useful Life (RUL) prediction for industrial systems, the pursuit of prediction accuracy must be balanced against the hardware costs of model operation and the reliability of prediction results. To resolve these challenges, we introduce LiST, an all-linear-layer spatial-temporal feature extractor integrated with uncertainty estimation, specifically designed for processing sensor multivariate time series (MTS) data. Unlike traditional linear models that flatten MTS and thus neglect their spatial-temporal dependencies, LiST's linear layers act on both the sensor and time dimensions of MTS that can extract spatial and temporal features like GNN and RNN models. Through performance comparisons on four RUL prediction datasets, LiST uses only 66.1% of the parameters, achieves comparable accuracy to state-of-the-art GNN and RNN models, obtains the best results on two datasets with up to a 21.6% improvement in Score, and enhances training efficiency by 3.2 times. Additionally, LiST can predict RUL with uncertainty estimation and precisely disentangle epistemic and aleatoric uncertainties, thus enhancing the model's practicality and reliability in real-world industrial applications.

关键词： RUL Prediction Spatial-Temporal Feature time Series Reliable System Uncertainty Estimation

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Quantum computing for Complex Energy systems: A Review 6

Quantum Computing for Complex Energy Systems: A Review

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6th international conference on Data-Driven Optimization of Complex systems (DOCS)

作者： Heinen, Xander Chen, Hao Univ Twente Behav Management & Social Sci BMS Enschede Netherlands Univ Twente Dept High Tech Business & Entrepreneurship IEBIS Enschede Netherlands

ISBN: (纸本)9798350377859;9798350377842

Quantum computing holds promise for addressing previously unsolvable problems, particularly within complex energy systems driven by big data. This research employs a semi-systematic literature analysis to identify and categorise popular quantum algorithms with potential applications in these systems. The algorithms are divided into two main groups: quantum chemical simulation algorithms and quantum optimisation algorithms. Quantum chemical simulations can model molecules, facilitating the discovery of advanced materials and technologies for complex energy systems. Meanwhile, quantum optimisation algorithms aim to enhance energy production efficiency by optimising the grid's energy flow and smart energy storage. A significant challenge across all algorithms is the current hardware limitations, as they require more processing power than is presently available. Additionally, these algorithms necessitate precise initial parameter settings, necessitating accurate representation of real-world scenarios. Overcoming these challenges could enable quantum computing to enhance the efficiency and effectiveness of complex energy systems significantly.

关键词： Quantum computing Algorithms Quantum circuits qubits Complex energy systems

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