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检索条件"任意字段=International Conference on Embedded Computer Systems -Architectures, Modeling and Simulation"

共 2877 条记录，以下是141-150 订阅

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ERSCHED: An Efficient and Reliable Scheduler for Workflows in Multi-Edge systems

ERSCHED: An Efficient and Reliable Scheduler for Workflows i...

引用

Intelligent Technology and embedded systems (ICITES), international conference on

作者： Linhui Wang Liying Li Yue Zeng Peijin Cong Junlong Zhou School of CSE Nanjing University of Science and Technology Nanjing China National Mobile Communications Research Laboratory Southeast University Nanjing China

ISBN: (数字)9798331506292

ISBN: (纸本)9798331506308

With the rise of edge computing, many users are turning to hosting and running workflow applications in multi-edge environments. This approach can effectively address issues such as resource limitations, low reliability, and high costs that are often encountered in single-edge environments. However, the heterogeneity of resources in multi-edge environments and the differences among edge service providers (in terms of technical architectures, service types, pricing models, etc.) make scheduling tasks in a multi-edge system quite complex. As a result, effectively utilizing multi-edge resources to efficiently and reliably schedule and execute workflows has become a crucial challenge in multi-edge environments. This paper concentrates on the multi-objective workflow scheduling optimization problem when facing reliability constraints in multi-edge environments. We design an efficient and reliable scheduler, namely ERSCHED, for executing workflows in multi-edge systems, with the goal of optimizing makespan and cost simultaneously under reliability constraints. We implement extensive experiments on both real-world and synthetic workflows to validate the efficacy of our scheduler ERSCHED. simulation results show that ERSCHED can derive better makespan-cost trade-off fronts with significantly higher HyperVolume than similar peer methods. The average improvement of HyperVolume realized by ERSCHED is 48.7%.

关键词： Schedules Costs Scheduling algorithms simulation computer architecture Pricing Reliability engineering Turning Service-oriented architecture Sorting

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Improving Reliability of STT-MRAM-Based Smart Material Implication

Improving Reliability of STT-MRAM-Based Smart Material Impli...

引用

IEEE conference on Nanotechnology

作者： Marco Lanuzza Tatiana Moposita Department of Computer Engineering Modeling Electronics and Systems University of Calabria Rende Italy

ISBN: (数字)9798350386240

ISBN: (纸本)9798350386257

Logic-in-Memory (LIM) architectures, particularly those based on Spin-Transfer Torque Magnetic Random-Access Memory (STT-MRAM), offer a promising solution to over-come the von-Neumann bottleneck of traditional computing platforms. The smart material implication (SIMPLY) LIM scheme has been proposed to execute computations efficiently within memory units. This paper presents reliability enhanced STT-MRAM-based SIMPLY and sFALSE operations, assessing improvements in read margin (RM) and bit error rate (BER). Furthermore, the energy efficiency of the SIMPLY scheme is evaluated for the implementation of some logic operators such as NAND, XOR, and XNOR. simulation results show that SIMPLY + scheme results to be energy efficient, while assuring a 3.7× larger RM as compared to the conventional SIMPLY.

关键词： Torque simulation High performance computing Bit error rate Memory management Materials reliability Energy efficiency

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modeling and Optimizing the 5G RRC State Machine to Reduce Terminal Energy Consumption

Modeling and Optimizing the 5G RRC State Machine to Reduce T...

引用

international conference on computer and Communications (ICCC)

作者： Jingbo Yang Yantai Vocational College Yantai China

ISBN: (数字)9798331507077

ISBN: (纸本)9798331507084

With the popularization and wide application of mobile terminals, addressing the issues of substantial energy consumption, low utilization of radio resources, and lackluster compatibility of conventional energy management strategies becomes significantly important. In this paper, we propose an enhanced Radio Resource Control (RRC) state machine, eRRC, to reduce the energy consumption of 5G user equipment. The basic idea is to dynamically configure the state parameters in real time according to the traffic behaviors when transmission. Specifically, we self-develop an energy monitor embedded in the terminal operation system, which can meticulously record the nuanced energy consumption patterns during each traffic transmission stage. Further, eRRC adapts the parameter setup and the activation time of each state when state machine transition. simulation experiments illustrate that compared with the prevailing terminal energy management methods, eRRC can realize an average energy conservation of 8.26%. Also, its lightweight implementation is suitable for other Internet of Things devices.

关键词： Energy consumption Adaptation models 5G mobile communication Computational modeling Energy conservation Real-time systems Internet of Things Monitoring

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MNIST Image Classification Using Convolutional Neural Networks

引用

international conference on modeling, simulation and Optimization, CoMSO 2021

作者： Choudhuri, Ashesh Roy Thakurata, Barnali Guha Debnath, Bipsa Ghosh, Debanjana Maity, Hrittika Chattopadhyay, Neela Chakraborty, Rupak Department of Computer Science and Engineering Guru Nanak Institute of Technology Kolkata India

ISBN: (纸本)9789811908354

Convolutional neural network (CNN) holds the current research interest in the ever-evolving image classification field. Accurate classifying the image data with minimum of time is highly desired. But the traditional CNN architecture often fails to generate the appropriate outcome for large dataset. So, a modified approach of CNN is proposed here which is the combination of data augmentation and batch normalization embedded with CNN. Nowadays, identifying or classifying digits accurately with variety of modes is really a task of challenge. The advantages of the proposed approach are noted when it is applied to the popular MNIST dataset used for digit classification. The proposed approach has been compared with some existing techniques and results infer that the validation, training loss, and testing accuracy of the proposed approach are more superior as compared to the state-of-art approaches. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Convolutional neural networks

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A Critical Study on Hybrid Computing and Its Current Influences and Future Challenges

A Critical Study on Hybrid Computing and Its Current Influen...

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Intelligent and Innovative Technologies in Computing, Electrical and Electronics (IITCEE), international conference

作者： Praveen K V Joe Prathap P. M L Sherin Beevi W Vinil Dani Department of Information Technology St. Peter’s College of Engg. and Tech. Chennai India Department of Computer Science and Engineering R.M.D. Engineering College India Department of EEE St. Peter’s College of Engg. and Tech. Chennai India

ISBN: (数字)9798331515911

ISBN: (纸本)9798331515928

This research focuses on the evolving field of hybrid computing, which combines traditional computing architectures with advanced computational paradigms like quantum computing, neuromorphic computing, or cloud-edge computing. Hybrid computing involves integrating multiple computing models or systems to leverage their complementary strengths. For example, combining classical computing with quantum or neuromorphic approaches to solve complex problems more efficiently. The goal is to enhance computational power, increase efficiency, and tackle problems that are difficult for traditional computing models alone. Emerging technologies, including quantum computing, machine learning, and AI, are driving the need for hybrid systems. These technologies enable the handling of more complex computations that classical systems struggle with, such as optimization, simulation, and cryptography. The integration of cloud and edge computing is also central to hybrid computing, enabling decentralized computation and processing closer to data sources. Combining different computing paradigms requires seamless communication and integration between systems, which is a significant technical challenge. Main challenging areas are scalability, software and hardware compatibility, energy efficiency and security. There is a need for standard protocols and frameworks to enable smoother integration of various computing systems. New algorithms that can take advantage of hybrid architectures need to be developed. Depend on applications significant advancements in hardware, including quantum processors, neuromorphic chips, and integrated hybrid processors required. The rise of hybrid computing will also bring ethical concerns, such as the impact on jobs, privacy, and inequality in access to advanced technologies. The study concludes by emphasizing the transformative potential of hybrid computing while acknowledging the technical, economic, and societal hurdles that need to be overcome. It highlights

关键词： Quantum computing Program processors Scalability Computational modeling Biological system modeling Standards organizations Machine learning Organizations Hardware Optimization

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Energy Efficient Computing with Heterogeneous DNN Accelerators 3

Energy Efficient Computing with Heterogeneous DNN Accelerato...

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IEEE 3rd international conference on Artificial Intelligence Circuits and systems (AICAS)

作者： Hossain, Md Shazzad Savidis, Ioannis Drexel Univ Integrated Circuits & Elect Lab Philadelphia PA 19104 USA

ISBN: (纸本)9781665419130

The exploration of custom deep neural network (DNN) based accelerators for highly energy constrained edge devices with on-device intelligence is gaining traction in the research community. Despite the superior throughout and performance of custom accelerators as compared to CPUs or GPUs, the energy efficiency and versatility of state-of-the-art DNN accelerators is constrained due to the limited scope of monolithic architectures, where the entire accelerator executes only one model at any given time. In this paper, a multi-voltage domain heterogeneous DNN accelerator architecture is proposed that simultaneously executes multiple models with different power-performance operating points. The proposed architecture and circuits are evaluated with SPICE simulation in a 65 nm CMOS technology. The simulation results indicate that the proposed heterogeneous architecture improves the energy efficiency to 2.04 TOPS/W, while the conventional monolithic and single voltage domain architecture exhibits an energy efficiency of 0.0458 TOPS/W. In addition, the total power consumption of the accelerator SoC is reduced to 1.34 W as compared to the 3.72 W consumed by the baseline architecture when all multiply-and-accumulate (MACs) units operate at a voltage of 0.45 V.

关键词： Semiconductor device modeling Power demand Computational modeling Memory management Neural networks computer architecture Throughput

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Benchmark Analysis of YOLOv8 for Edge AI Video-Surveillance Applications

Benchmark Analysis of YOLOv8 for Edge AI Video-Surveillance ...

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IEEE/ASME international conference on Mechtronic and embedded systems and Applications, MESA

作者： Daniele Berardini Lucia Migliorelli Lorenzo Cardoni Christian Parente Alessandro Rongoni Daniele Sergiacomi Adriano Mancini Department of Information Engineering Polytechnic University of Marche Ancona Italy

ISBN: (数字)9798331516239

ISBN: (纸本)9798331516246

Video surveillance systems (VSSs) are crucial for identifying crimes involving handheld weapons, but their effectiveness is limited by high operational costs and the need for continuous human monitoring. To overcome these challenges, research has turned to deep learning (DL) to automate weapon detection. Early efforts focused on detecting weapons in images using resource-intensive frameworks – and, thus, unaffordable – and relying on datasets that poorly represent real-world settings. Given the state-of-the-art limitations, this work introduces an edge artificial intelligence (AI)-based approach which explores the YOLOv8 architecture across different variants (nano, small, medium, large, extra-large), evaluated on a custom built dataset (WeaponSenseV2). Using the NVIDIA Jetson Nano as an edge device, the proposed study evaluates performance of the architectures on the custom-built WeaponSenseV2 dataset, which is designed to more accurately reflect real-world scenarios than existing datasets. By focusing on the intersection of edge computing and DL, this research aims to reduce the operational costs and computational demands associated with traditional VSSs. The results enrich the ongoing discussions on leveraging technological advancements to enhance public safety, providing valuable perspectives on the real-world deployment of scalable and efficient systems for detecting weapons.

关键词： Performance evaluation Costs Weapons Computational modeling Image edge detection computer architecture Edge AI Video surveillance Nanoscale devices Monitoring

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modeling Hydrodynamic Diffusion Processes using Spatio-Temporal Deep Neural Networks with Environmental Physical-Coupled Constraints

Modeling Hydrodynamic Diffusion Processes using Spatio-Tempo...

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IEEE international conference on systems, Man and Cybernetics

作者： Lei Jia Neil Yen Yan Pei Graduate School of Computer Science and Engineering University of Aizu Aizuwakamatsu Fukushima Japan

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

The simulation and analysis of complex spatiotemporal systems are crucial for expressing and solving chaotic dynamical systems such as those in Earth and environmental sciences. Understanding and computing physical processes, re-actions, or substance transport typically relies on control equations. This paper aims to explore a novel research paradigm by enhancing the physical network coupling structure to construct predictive models for fluid dynamics systems, simulating spatiotemporal dynamical processes of substance transformation in the domain of environmental physics. In particular, when addressing problems involving the non-homogeneous 2D fluid dynamics equations, the characteristic parameters of the phys-ical processes were redefined. This was achieved by encoding hard boundary conditions and designing appropriate neural network architectures to mitigate over-fitting issues during the prediction of parameterized dynamical systems. Comparative experiments involving five benchmark physics-informed neural network methods emphasize the significant improvement in capturing time-varying features and prediction accuracy brought by the proposed approach. Through various water cycle scenarios, the model's estimation ability for diffusion fields is validated, focusing on analyzing the influence of data errors and sample size on the computational results of this deep neural network. Notably, the proposed method exhibits higher robustness to outlier observations under extreme conditions.

关键词： Fluid dynamics Computational modeling Process control Artificial neural networks Predictive models Mathematical models Robustness Spatiotemporal phenomena Dynamical systems Water cycle

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TCAD simulation of Radiation Effects on 180 nm Logic Inverters

TCAD Simulation of Radiation Effects on 180 nm Logic Inverte...

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Thermal, Mechanical and Multiphysics simulation and Experiments in Micro-Electronics and Micro-systems EuroSimE

作者： Eike Trumann Amen Allah Bahri Gia Bao Thieu Kirsten Weide-Zaage Dorian Von Wolff Andre Bausen Alexander Müller Guillermo Payá-Vayá Chair for Chip Design for Embedded Computing Technische Universität Braunschweig Braunschweig Germany RESRI Group Institute of Microelectronic Systems (IMS) Leibniz Universität Hannover Hannover Germany Bundeswehr Research Institute for Protective Technologies and CBRN Protection (WIS) Munster Germany

ISBN: (数字)9798350393002

ISBN: (纸本)9798350393019

This work presents a physically based simulation evaluation of the effects of single ionizing particles and displacement damage on logic inverter gates in a complementary metal-oxide semiconductor (CMOS) with 180 nm structure size. A technology computer-aided-design (TCAD) model is combined with a radiation model to allow the simulation of charged particles with a linear energy transfer (LET) between 1 and 100 $\frac{M e V \cdot c m^simulation}{m g}$ . For displacement damage simulation, proton energies between 1 and 10 MeV and neutron energies between 1.6 and 14 MeV have been evaluated to estimate the resulting transistor threshold voltages.

关键词： Semiconductor device modeling Protons Micromechanical devices Neutrons Pulse inverters Threshold voltage Microelectronics Transistors Periodic structures MOS devices

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Spatiotemporal Traffic Flow Prediction Using CNN-LSTM architectures

Spatiotemporal Traffic Flow Prediction Using CNN-LSTM Archit...

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international conference on Intelligent Computing in Data Sciences (ICDS)

作者： Yassine Ennaji Nada Faqir Jaouad Boumhidi LISAC Laboratory Sidi Mohamed Ben Abdellah University Fez Morocco

ISBN: (数字)9798350351200

ISBN: (纸本)9798350351217

In this paper, we present a spatiotemporal model for traffic flow prediction using a CNN-LSTM architecture. The proposed method integrates multivariate inputs, including position, velocity, and traffic light states, into a deep learning framework. Our approach leverages the simulation capabilities of SUMO to generate realistic traffic data, which is then used to train the CNN-LSTM model. We evaluate the performance of the model using standard error metrics such as Mean Squared Error (MSE) and Root Mean Squared Error (RMSE). The results demonstrate the effectiveness of the CNN-LSTM model in capturing the complex dynamics of urban traffic and its potential for real-time traffic prediction applications in smart cities.

关键词： Measurement Smart cities Computational modeling Transportation computer architecture Predictive models Data models Real-time systems Spatiotemporal phenomena Standards

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