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

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Duration-based Instruction Cache Locking

Duration-based Instruction Cache Locking

引用

ieee international conference on embedded and real-time computing systems and applications (RTCSA)

作者： Wafic Lawand Rodolfo Pellizzoni University of Waterloo

ISBN: (数字)9798350387957

ISBN: (纸本)9798350387964

Cache locking is a commonly used mechanism to improve both performance and predictability for embedded programs. Dynamic cache locking methods proposed in the literature, where the locked content is modified during execution, require inserting locking and unlocking instructions in the program’s code. In this paper, we introduce a novel hardware mechanism that leverages the LRU age bits to perform duration-based locking. Our proposed mechanism dynamically locks and unlocks cache lines for different durations at run-time, without the need to modify the program’s code. We further devise a heuristic that analyzes a program’s loop structure and selects the set of addresses to be locked in a L1 instruction cache alongside their locking durations. Evaluation results show that our duration-based locking mechanism achieves comparable results to the dynamic approach while substantially reducing the initialization overhead and avoiding program code modifications.

关键词： Codes embedded systems real-time systems Hardware

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TrustMesh: A Blockchain-Enabled Trusted Distributed computing Framework for Open Heterogeneous IoT Environments 22

TrustMesh: A Blockchain-Enabled Trusted Distributed Computin...

引用

22nd ieee international conference on Software Architecture, ICSA 2025

作者： Rangwala, Murtaza Buyya, Rajkumar Laboratory School of Computing and Information Systems Australia

ISBN: (纸本)9798331520908

The rapid evolution of Internet of Things (IoT) environments has created an urgent need for secure and trust-worthy distributed computing systems, particularly when dealing with heterogeneous devices and applications where centralized trust cannot be assumed. This paper proposes TrustMesh, a novel blockchain-enabled framework that addresses these challenges through a unique three-layer architecture combining permissioned blockchain technology with a novel multi-phase Practical Byzantine Fault Tolerance (PBFT) consensus protocol. The key innovation lies in TrustMesh's ability to support non-deterministic scheduling algorithms while maintaining Byzantine fault tolerance, features traditionally considered mutually exclusive in blockchain systems. The framework supports a sophisticated resource management approach that enables flexible scheduling decisions while preserving the security guarantees of blockchain-based verification. Our experimental evaluation using a real-world cold chain monitoring scenario demonstrates that TrustMesh successfully maintains Byzantine fault tolerance with fault detection latencies under 150 milliseconds, while maintaining consistent framework overhead across varying computational workloads even with network scaling. These results establish TrustMesh's effectiveness in balancing security, performance, and flexibility requirements in trustless IoT environments, advancing the state-of-the-art in secure distributed computing frameworks. © 2025 ieee.

关键词： Edge computing

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Enhancing Traffic Management with embedded Machine Learning for Vehicle Detection

Enhancing Traffic Management with Embedded Machine Learning ...

引用

2023 international conference on Microelectronics, ICM 2023

作者： Talip, Mohamad Sofian Abu Razak, Mohd Zulhakimi Ab Mohamad, Mahazani Khairuddin, Anis Salwa Mohd Izam, Tengku Faiz Tengku Mohmed Noor Azizan, Azizul University of Malaya Faculty of Engineering Department of Electrical Engineering Kuala Lumpur50603 Malaysia Institute of Microengineering and Nanoelectronics Universiti Kebangsaan Malaysia Bangi Selangor 43600 Malaysia Universiti Teknologi Malaysia Razak Faculty of Technology and Informatics Advanced Informatics Department Kuala Lumpur54100 Malaysia

ISBN: (纸本)9798350380828

In recent years, vehicle detection has become vital for applications ranging from autonomous driving to traffic control, surveillance, and monitoring. The demand for efficient real-time detection systems has surged, prompting the integration of machine learning algorithms into embedded platforms as a promising approach. This paper focuses on developing a robust and efficient system deployable on the NVIDIA Jetson Nano 2GB Developer Kit. The system harnesses machine learning algorithms tailored for resource-constrained embedded systems to achieve high detection accuracy in realtime. The process encompasses data preparation, preprocessing, feature extraction, and classification. Deep learning models used are You Only Look Once (YOLO) algorithm, YOLOv5n, and YOLOv7-tiny, trained on labeled datasets to classify regions of interest based on unique vehicle attributes. For inference on the Jetson Nano, both are chosen for their real-time capabilities and high object detection accuracy, are employed. To leverage the Jetson Nano's GPU power, NVIDIA's Compute Unified Device Architecture (CUDA) toolkit is installed, enabling parallel computing and deep learning model optimization. Results indicate that YOLOv7-tiny achieves the better precision-confidence at 92.7% and a recall-confidence of 94% compared to YOLOv5n. This study also uses various other evaluation metrics such as accuracy, precision, recall, confusion matrix, and F1 score to measure system performance and examine computational efficiency, to help in the selection of appropriate models for embedded systems. © 2023 ieee.

关键词： embedded systems

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Transaction Data Management Optimization Based on Multi-Partitioning in Blockchain systems 21

Transaction Data Management Optimization Based on Multi-Part...

引用

21st ieee international Symposium on Parallel and Distributed Processing with applications, 13th ieee international conference on Big Data and Cloud computing, 16th ieee international conference on Social computing and Networking and 13th international conference on Sustainable computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Zhang, Jingyu Hou, Xingxing Teng, Yilong Luo, Lailong Changsha University of Science and Technology School of Computer and Communication Engineering Changsha China National University of Defense Technology Science and Technology on Information Systems Engineering Laboratory School of Systems Engineering Changsha China

ISBN: (纸本)9798350329223

Unspent Transaction Output (UTXO) is part of the transaction data set, which represents the digital cryptocurrency asset in transaction-based blockchain systems. The data management capability, storage method and occupied space of UTXOs will greatly affect the running efficiency and the verification performance of blockchain systems. Especially, with the popularity of blockchain technology, the relevant UTXO data sets have been growing, and all the stored data can no longer be almost completely stored in memory. How should the UTXO transaction data be stored and managed at this time, it is an urgent issue to be solved in bitcoin-like blockchain systems. This paper provides a blockchain transaction data management optimization mechanism based on multi-partitioning. First, we analyze the influencing factors of transactions through real blockchain data. The proposed method can evaluate the time interval and transaction frequency factors, and use the received information to realize the efficient transaction data storage. In our design, UTXOs with lower likelihood to be used in new transactions will be stored in the disk, and the other UTXOs with higher likelihood to be used in new generated transactions should be stored in the cache. This approach aims to minimize memory consumption for the transaction data sets, accelerate UTXO access time during block verification, and ultimately decrease the overall time required for verification, leading to efficient UTXO transaction data management. Finally, the effectiveness of the proposed optimization mechanism is verified through theoretical analysis and simulation experiments, and the UXTO access time has been reduced compared with state-of-the-art methods. © 2023 ieee.

关键词： Information management

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Heuristic Scheduling of Streaming applications for Energy Efficiency on Heterogeneous Multicores 25

Heuristic Scheduling of Streaming Applications for Energy Ef...

引用

25th ieee international conferences on High Performance computing and Communications, 9th international conference on Data Science and systems, 21st ieee international conference on Smart City and 9th ieee international conference on Dependability in Sensor, Cloud and Big Data systems and applications, HPCC/DSS/SmartCity/DependSys 2023

作者： Litzinger, Sebastian FernUniversitat in Hagen Faculty of Mathematics and Computer Science Germany

ISBN: (纸本)9798350330014

A recent manifestation of the trend towards heterogeneous computing is the introduction of heterogeneous multicore processors into the desktop segment, offering the potential for unprecedented energy efficiency in the execution of parallel applications. Such an application may be launched repeatedly, as it is, e. g., the case with stream processing applications, and at the same time exhibit dynamic behavior, for instance due to varying input quality. The scheduler is then challenged with delivering premium schedules within a short time frame to tap the full potential for energy efficiency as well as react to shifting application behavior. We present a heuristic approach to heterogeneous application scheduling, which also takes into account prevalent hardware limitations with regard to frequency scaling. The scheduling heuristic is capable of accomplishing high energy efficiency of the resulting schedules in conjunction with rapid scheduling. This is demonstrated experimentally in a comparison to two static schedulers based on linear programming, which-contrary to our heuristic-cannot account for dynamic application behavior but serve to provide reference values for energy consumption and scheduling time. © 2023 ieee.

关键词： Linear programming

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Response-time Analysis of Fault-Tolerant Hard real-time systems Under Global Scheduling

Response-time Analysis of Fault-Tolerant Hard Real-Time Syst...

引用

ieee international conference on embedded and real-time computing systems and applications (RTCSA)

作者： Pourya Gohari Jeroen Voeten Mitra Nasri Eindhoven University of Technology (TU/e) Eindhoven The Netherlands

real-time systems are commonly used in safety-critical applications which require tasks to be completed before their deadlines, even in the presence of faults. Thus, fault tolerance becomes essential to ensure a certain level of reliability in safety-critical real-time systems [1]. To achieve fault tolerance in computer systems, redundancy can be implemented either in space (spatial redundancy) or time (time redundancy) [2]. Unlike spatial redundancy which involves increasing hardware resources, time redundancy focuses on re-execution or multiple executions of software on the same hardware resources [3] and therefore is better suited for embedded systems with limited cost and size constraints that are subject to transient faults more often than permanent faults [2], [4].

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Adaptive Modulation and Coding in 5G Networks with Deep Learning

Adaptive Modulation and Coding in 5G Networks with Deep Lear...

引用

2024 ieee international conference on Communication, computing and Signal Processing, IICCCS 2024

作者： Gupta, Anjula Reddy, Nirmala Shnain, Ammar Hameed Revathi, K. Gopalakrishnan, S. Verma, Chetan K. Chandigarh Group of Colleges Chandigarh Engineering College Department of Computer Application Punjab Jhanjeri Mohali140307 India Ies College of Technology Department of Computer Science & Engineering M.P. Bhopal India The Islamic University College Of Technical Engineering Department Of Computers Techniques Engineering Najaf Iraq The Islamic University Of Al Diwaniyah College Of Technical Engineering Department Of Computers Techniques Engineering Al Diwaniyah Iraq Prince Shri Venkateshwara Padmavathy Engineering College Chennai India Hindustan Institute of Technology and Science Department of Information Technology Tamil Nadu Chennai603103 India Cvm University Mbit Gujrat India

ISBN: (纸本)9798350390759

As part of the present work, it aims to examine the use of deep learning in the improvement of AMC in 5G networks. While traditional AMC methods like the ones mentioned above are applicable to previous generations of the mobile networks, they are not optimal taking into consideration the complexities and the constantly evolving nature of 5G systems. Therefore, we suggest the use of convolutional and recurrent neural networks with the aim of employing deep learning in designing an adaptive system that is capable of predicting the optimal modulation and coding schemes given instantaneous CSI information. It covers data gathering, process modeling, and implementation on a platform designed for 5G consumer network. Thus, the improvement in the data throughput rate, the level of errors, and the degree of satisfaction, obtained through the experimental results, can confirm the optimal nature of the proposed approach. It can be seen that the deep learning-based AMC system is computationally efficient and can be embedded for real-time applications, and the performance of the system is further optimised with improving learning. To conclude, this study reveals the effectiveness of deep learning in transforming AMC in 5G networks, by offering a dependable and dynamic solution the wired and wireless telecommunications networks. In a future work more towards detailing of the model and finding new architectures for the system more refined system can be achieved. © 2024 ieee.

关键词： 5G mobile communication systems

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Mosaic: Detection and Categorization of I/O Patterns in HPC applications

Mosaic: Detection and Categorization of I/O Patterns in HPC ...

引用

2024 Workshops of the international conference for High Performance computing, Networking, Storage and Analysis, SC Workshops 2024

作者： Jolivel, Theo Tessier, Francois Monniot, Julien Pallez, Guillaume Univ Rennes Inria CNRS IRISA Rennes France

ISBN: (纸本)9798350355543

With the gap between computing power and I/O performance growing ever wider on HPC systems, it is becoming crucial to optimize how applications perform I/O on storage resources. To achieve this, a good understanding of application I/O behavior is an essential preliminary step. In this paper, we introduce Mosaic, a method for categorizing applications according to their I/O behavior. We first propose an abstraction for characterizing I/O operations in terms of periodicity, temporality and metadata access. We then present a set of segmentation-based techniques for quickly and automatically detecting meaningful data access patterns. In the end, Mosaic is able to characterize a full set of real-world I/O traces from the Blue Waters supercomputer with 92% accuracy. © 2024 ieee.

关键词： analysis characterization I/O traces

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ONNX-To-Hardware Design Flow for Adaptive Neural-Network Inference on FPGAs 1

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

作者： Manca, Federico Ratto, Francesco Palumbo, Francesca Univ Sassari Piazza Univ 21 I-07100 Sassari Italy Univ Cagliari Via Marengo 2 I-09123 Cagliari Italy

ISBN: (数字)9783031783807

ISBN: (纸本)9783031783791;9783031783807

The challenges involved in executin Neural Networks (NNs) at the edge include providing diversity, flexibility, and sustainability. That implies, for instance, supporting evolving applications and algorithms energy-efficiently. Using hardware (hw) or software accelerators can deliver fast and efficient computation of the NNs, while flexibility can be exploited to support long-term adaptivity. Nonetheless, handcrafting a NN for a specific device, despite the possibility of leading to an optimal solution, takes time and experience, and that's why frameworks for hw accelerators are being developed. This work, starting from a preliminary semi-integrated ONNX-to-hardware toolchain [23], focuses on enabling Approximate computing (AC) leveraging the distinctive ability of the original toolchain to favor adaptivity. The goal is to allow lightweight adaptable NN inference on FPGAs at the edge.

关键词： Convolutional Neural Networks Approximate computing FPGAs Cyber-Physical systems

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A 0.36nW, 820μm2, 32kHz Conduction-Angle-Adaptive Crystal Oscillator in 28nm CMOS for real-time Clock applications 72

A 0.36nW, 820μm2, 32kHz Conduction-Angle-Adaptive Crystal O...

引用

72nd ieee international Solid-State Circuits conference, ISSCC 2025

作者： Wang, Peng Wang, Manyu Dai, Guangnan Cao, Yujia Pan, Sining Zhang, Yihan Hong Kong University of Science and Technology Hong Kong Tsinghua University Beijing China

ISBN: (纸本)9798331541019

32.768kHz (32kHz) crystal oscillators (XOs) are widely used in real-time clocks (RTC) embedded in various electronic systems. Their performance is critical in battery-powered Internet-of-Things (IoT) sensor nodes when serving as wake-up timers. Extending the battery life requires an ultra-low-power (ULP) XO, as it must stay always-on. It also requires good frequency stability;otherwise, the system has to extend its communication guard times for synchronization, in which power-hungry transceivers need to stay active, leading to extra waste in power [1]. These demands have motivated much recent research to seek alternatives to the classic inverter-based Pierce oscillator, which is simple in design but typically consumes 10 to 100nW of power [2-5]. Pulse-injection XOs (PIXOs) are popular due to their sub-nW power consumption. Their power reduction comes from using a tiny conduction angle in the form of short pulses for Class-C-like operations. However, positioning current injections to the optimum timing typically requires generating an accurate delay, making pulse generators area-consuming [6-10]. Moreover, these methods cannot initiate the oscillation, as the zero-crossing instants, which are the starting times of the delay, are only available once an oscillation is present. These PIXOs, hence, need to embed separate start-up circuits to build up the initial oscillation, necessitating extra control logic and facing robustness challenges under accidental but strong interferences. A compact and easy-to-use ULP XO remains in demand from the cost-sensitive IoT market. © 2025 ieee.

关键词： Crystal oscillators

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