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共 7932 条记录，以下是321-330 订阅

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An Effective Technique for Enhancing the Energy Efficiency of Digital Data Processing by Using Xilinx and Iot Sensors to Inexact Arithmetic Circuit

An Effective Technique for Enhancing the Energy Efficiency o...

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2024 International Conference on Sustainable Computing and Integrated Communication in Changing Landscape of AI, ICSCAI 2024

作者： Raveendra Babu, P. Bharath Kumar, K. Gampala, Divya Kishore, Badavath Department of ECE CMR College of Engineering & Technology Telangana Hyderabad India Department of ECE CMR Technical Campus Telangana Hyderabad India Department of ECE CMR Engineering College Telangana Hyderabad India Department of ECE CMR Institute of Technology Telangana Hyderabad India

ISBN: (纸本)9798350355338

There is a growing need for portable autonomous systems in several fields, including as wearable technology, Internet of Things (loT), and medical applications. Autonomous and networked sensors are employed in the majority of portable systems;each sensor includes many input channels (such as touch) that are closely connected to an embedded processor and a power source. The embedded computing unit should extract critical data locally using sophisticated techniques. This makes real-time tasks more difficult and puts more demand on power consumption. A promising approach to energy-efficient data processing is approximate or inexact computing, which adjusts computation precision to suit the specific requirements of the application while consuming less power. This work aims to enhance the energy efficiency of digital data processing for sensors by utilizing inexact arithmetic circuits. Xilinx 14.7 is used for simulation and synthesis of suggested design. © 2024 ieee.

关键词： Steganography

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A real-time Execution System of Multimodal Transformer through PIM-GPU Collaboration 24

A Real-time Execution System of Multimodal Transformer throu...

引用

61st ACM/ieee Design Automation Conference, DAC 2024

作者： Ji, Shengyi Liu, Chubo Ding, Yan Liao, Qing Tang, Zhuo College of Information Science and Engineering Hunan University Changsha China Department of Computer Science and Technology Harbin Institute of Technology Shenzhen China

ISBN: (纸本)9798400706011

Multimodal transformer excels in various applications, but faces great challenges such as high memory consumption and limited data reuse that hinder real-time performance. To address these issues, we propose a processing-in-memory (PIM)-GPU collaboration oriented compiler to accelerate the multimodal transformers. The PIM-GPU collaboration adapts well to multimodal transformers and significantly accelerates model inference. In addition, we introduce a tailored PIM allocation algorithm for variable-length inputs to further improve computation efficiency. Experimental results show that our scheme can achieve an average 15x end-to-end speedup. © 2024 Copyright is held by the owner/author(s). Publication rights licensed to ACM.

关键词： Graphics processing unit

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Context-based Latency Guarantees Considering Channel Degradation in 5G Network Slicing 43

Context-based Latency Guarantees Considering Channel Degrada...

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43rd ieee real-time Systems symposium (RTSS)

作者： Nota, Andrea Saidi, Selma Overbeck, Dennis Kurtz, Fabian Wietfeld, Christian TU Dortmund Univ Chair Embedded Syst Dortmund Germany TU Dortmund Univ Commun Networks Inst Dortmund Germany

ISBN: (数字)9781665453462

ISBN: (纸本)9781665453462

Mission critical applications in domains such as Industry 4.0, autonomous vehicles or smart grids are increasingly dependent on flexible, yet highly reliable communication systems. The Fifth Generation of mobile Communication Networks (5G) promises to support critical communications on a single unified physical communication network through a novel approach known as network slicing. We focus in this work on contextbased hard performance guarantees by formalizing an analytical method for bounding response times in critical systems. This approach allows to consider different contexts based on models of degradation of channel quality, and avoids a global highly pessimistic worst-case bound computed for worst possible channel conditions. We demonstrate that the proposed method for computing context-based response times guarantees successfully bounds results obtained in realistic mobility scenarios using a machine-learning based 5G simulation framework.

关键词： 5G Network Slicing Latency Guarantees Channel Degradation

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DualLight: Intelligent Traffic Signal Control Method Based on Deep Reinforcement Learning 22

DualLight: Intelligent Traffic Signal Control Method Based o...

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22nd ieee International symposium on Parallel and Distributed Processing with applications, ISPA 2024

作者： Tang, Huanqiang He, Fan Cheng, Yanfen Shao, Xun Wuhan University of Technology School of Computer Science and Artificial Intelligence China Toyohashi University of Technology Department of Electrical and Electronic Information Engineering Japan

ISBN: (纸本)9798331509712

In recent years, research combining max pressure (MP) with reinforcement learning for traffic signal control has become a hot topic in the field of intelligent transportation. However, existing MP methods ignore the importance of moving vehicles, which prevents the model from focusing on sufficient key information when extracting state features, thus making it suffer from unstable learning, slow convergence, and limited performance during the training process. To address this problem, this paper proposes DualLight, a signal control algorithm based on deep reinforcement learning. In DualLight, a new traffic state representation, is designed, which can help the model extract key information at intersections. To stabilize the learning process of the agent and accelerate the convergence speed, the DMP control method is designed as an expert method for agent imitation in the pretraining module, through which the parameters of the model are initialized. Comprehensive experiments on several real-world datasets show that DualLight improves its performance on the average travel time metric by an average of 17.05%, which significantly improves the efficiency of the transportation network. © 2024 ieee.

关键词： Multi agent systems

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Efficient Algorithm Analysis Of Kidney Images On FPGA

Efficient Algorithm Analysis Of Kidney Images On FPGA

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2025 ieee International Students' Conference on Electrical, Electronics and Computer Science, SCEECS 2025

作者： Thanuja, G.V. Ramya, S. Manipal Institute of Technology Manipal Academy of Higher Education Dept. Electronics and Communication Engineering Karnataka Manipal576104 India

ISBN: (纸本)9798331529833

The field-programmable gate array (FPGA) offers an effective solution to meet the high-performance requirements of real-time digital signal processors. IP cores developed on FPGAs benefit from the programmable logic's flexibility, efficient timing, and adaptability in algorithm modification, coupled with the processing power provided by the embedded processor. Integrating image processing into this sector is an ideal addition, especially in the growing field of edge detection, which is crucial in areas like image pattern recognition, machine learning, and data processing. This paper presents a case study focused on kidney CT scan images for edge detection, utilizing the Sobel and Canny edge detection techniques on the target FPGA device, xc7z020clg484-1. An IP core was designed and generated using VIVADO software, converting image pixels into binary form for pre-processing through these edge detection algorithms. The algorithms were simulated and synthesized on the xc7z020clg484-1 FPGA, with the entire design implemented in Verilog code to create the IP, which connects to the DMA controller throughout the system. This IP core can process multiple CT scan images simultaneously, making it valuable for various biomedical applications. The primary aim of this research is to compare the Sobel and Canny edge detection algorithms to identify the best approach for developing an IP core, evaluated by metrics such as total power consumption, CPU time, execution time, LUT count, and FF usage. The resulting IP core is efficient and conserves resources, making it suitable for other embedded applications. © 2025 ieee.

关键词： Image analysis

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Characterizing Soft-Error Resiliency in Arm's Ethos-U55 embedded Machine Learning Accelerator

Characterizing Soft-Error Resiliency in Arm's Ethos-U55 Embe...

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2024 ieee International symposium on Performance Analysis of Systems and Software, ISPASS 2024

作者： Tyagi, Abhishek Jeyapaul, Reiley Zhou, Chuteng Whatmough, Paul Zhu, Yuhao University of Rochester Department of Computer Science RochesterNY United States AustinTX United States ARM Inc Central Technology AustinTX United States Qualcomm AI Research BostonMA United States

ISBN: (纸本)9798350376388

As Neural Processing Units (NPU) or accelerators are increasingly deployed in a variety of applications including safety critical applications such as autonomous vehicle, and medical imaging, it is critical to understand the fault-tolerance nature of the NPUs. We present a reliability study of Arm's Ethos-U55, an important industrial-scale NPU being utilised in embedded and IoT applications. We perform large scale RTL-level fault injections to characterize Ethos-U55 against the Automotive Safety Integrity Level D (ASIL-D) resiliency standard commonly used for safety-critical applications such as autonomous vehicles. We show that, under soft errors, all four configurations of the NPU fall short of the required level of resiliency for a variety of neural networks running on the NPU. We show that it is possible to meet the ASIL-D level resiliency without resorting to conventional strategies like Dual Core Lock Step (DCLS) that has an area overhead of 100%. We achieve so through selective protection, where hardware structures are selectively protected (e.g., duplicated, hardened) based on their sensitivity to soft errors and their silicon areas. To identify the optimal configuration that minimizes the area overhead while meeting the ASIL-D standard, the main challenge is the large search space associated with the time-consuming RTL simulation. To address this challenge, we present a statistical analysis tool that is validated against Arm silicon and that allows us to quickly navigate hundreds of billions of fault sites without exhaustive RTL fault injections. We show that by carefully duplicating a small fraction of the functional blocks and hardening the Flops in other blocks meets the ASIL-D safety standard while introducing an area overhead of only 38 %. © 2024 ieee.

关键词： Radiation hardening

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real-time Classification of Dog Breeds Using the M5StickV 7

Real-Time Classification of Dog Breeds Using the M5StickV

引用

7th International Conference of Computer and Informatics Engineering, IC2IE 2024

作者： Putri, Desy Purnami Singgih Prasetyo, Eko Wahyu Buana, Putu Wira Udayana University Faculty of Engineering Department of Information Technology Bali Indonesia Bunda Mulia University Faculty of Information and Desain Department of Data Science Jakarta Indonesia

ISBN: (纸本)9798331505530

The accurate classification of dog breeds is a challenging task due to the subtle differences between breeds and the computational limitations of portable devices. Despite advancements in deep learning, there is a gap in the literature regarding the deployment of efficient and accurate models on resource-constrained embedded systems. This study addresses this gap by investigating the classification of nine dog breeds using a dataset of 1,080 images, processed through the MobileNetV1 architecture. The focus is on implementing this model within the M5StickV embedded system, a compact and portable device. The images underwent pre-processing, including resizing to 224 by 224 pixels and background removal. Transfer learning with Sipeed was utilized to fine-tune the model, which was subsequently converted to a Keras-compatible format for deployment. The resulting model demonstrated high accuracy (0.9833) and precision (0.9840), underscoring its robust performance. However, a slight delay in the classification process suggests the need for further optimization. This study highlights the potential of integrating advanced deep learning models with embedded systems for real-time, portable image classification, offering applications in pet identification and other fields. © 2024 ieee.

关键词： Deep learning

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Vineyard Thermal Stress Assessment through the Combination of In-Situ and Remote Sensing technology 18

Vineyard Thermal Stress Assessment through the Combination o...

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18th ieee Sensors applications symposium (SAS)

作者： Felicio, Teresa Postolache, Octavian Adrian Rodrigues, Mariana Jacob Sebastiao, Pedro ISCTE IUL Inst Telecomunicac IT IUL Lisbon Portugal

ISBN: (纸本)9798350323078

Precision viticulture is crucial for optimizing vineyard productivity and sustainability, particularly as climate change poses increasing threats to plant health. This work explores the development of an Internet of Things (IoT) system for the evaluation and mitigation of thermal stress in vineyards, an\ abiotic stress factor that can adversely affect growth and productivity. The proposed system combines in-situ sensing and remote sensing technologies, which also include the use of thermography imagery, to collect real-time data on parameters such as air relative humidity, soil moisture, air/leaf/soil temperature, and luminosity. Through the integration of different sensing channels, the study establishes correlations between the monitored parameters, enabling a more comprehensive assessment of vineyard health status with respect to thermal stress.

关键词： Precision Viticulture Internet of Things (IoT) Thermal Stress Abiotic Stress In-Situ Sensing Remote Sensing Thermography Imagery

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Combining TL and KD for Object Detection Model Compression 13

Combining TL and KD for Object Detection Model Compression

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Joint 13th International Conference on Soft Computing and Intelligent Systems and 25th International symposium on Advanced Intelligent Systems, SCIS and ISIS 2024

作者： Peng, Yuhan Lyu, Yan Zhao, Qiangfu The University of Aizu Computer Science and Engineering Aizuwakamatsu965-8580 Japan Muroran Institute of Technology Sciences and Informatics Muroran050a-8585 Japan

ISBN: (纸本)9798350373332

In modern society, object detection tasks have become extremely important due to their numerous applications in fields such as autonomous driving, surveillance, and healthcare. real-time detection is particularly crucial for many applications. However, with the advancements in deep learning-based object detection methods, high performance often comes with high computational resource requirements, posing challenges for real-time monitoring, especially on resource-constrained devices. To address this issue, we study model compression using both transfer learning (TL) and knowledge distillation (KD). Specifically, we use the YOLOv5n model as a test case to show the basic idea for improving the performance of a small student model using a larger dataset for TL and a larger model for KD. As for KD, we further investigate feature-based and output-based KD. Experimental results show that the small student model can be improved significantly using TL and KD, with the mean Average Precision (mAP0.5) being increased by 13.28% compared to the baseline model. These results demonstrate the effectiveness of TL and KD in achieving high detection performance while reducing computational demands, making them suitable for various real-world applications. © 2024 ieee.

关键词： Adversarial machine learning

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Research on HLS Implementation of Self-Calibrated Illumination Neural Network: Low-Light Image Enhancement 7

Research on HLS Implementation of Self-Calibrated Illuminati...

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7th ieee Information technology, Networking, Electronic and Automation Control Conference, ITNEC 2024

作者： Wang, Hongyu Zhang, Pingjuan School of Electrical and Electronic Engineering Anhui Science and Technology University Bengbu China

ISBN: (纸本)9798350370782

Low-light image enhancement is crucial in applications such as nighttime surveillance, autonomous driving, and medical imaging. Traditional image enhancement methods like histogram equalization and Laplacian transformation can improve image brightness and contrast to some extent but struggle with complex lighting variations and detail preservation. Recently, deep learning-based methods have demonstrated superior performance but typically rely on high-performance computing resources, making real-time processing on resource- constrained embedded devices challenging. The paper addresses this problem by proposing a Self-Calibrated Illumination Neural Network (SCINN) based low-light picture enhancement method and implementing it with hardware acceleration through the use of High-Level Synthesis (HLS) tools. SCINN employs a self-calibration mechanism to adaptively adjust illumination parameters based on input image features, enhancing low-light images. In particular, SCINN is composed of three primary modules: image improvement, illumination correction, and feature extraction. It makes use of a Convolutional Neural Network (CNN) to improve the original image, extract features, and produce illumination adjustment parameters. Experimental results demonstrate that SCINN excels in improving image brightness and contrast while preserving details. Compared to traditional methods, SCINN shows significantly better enhancement effects. Additionally, the hardware-accelerated implementation using HLS tools on an FPGA shows that SCINN achieves efficient image enhancement with low hardware resource consumption, significantly improving processing speed and meeting real-time requirements. © 2024 ieee.

关键词： Field programmable gate arrays (FPGA)

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