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Trikarenos: Design and Experimental Characterization of a Fault-Tolerant 28nm RISC-V-based SoC

IEEE Transactions on Nuclear Science 2025年

作者： Rogenmoser, Michael Wiese, Philip Forlin, Bruno Endres Gurkaynak, Frank K. Rech, Paolo Menicucci, Alessandra Ottavi, Marco Benini, Luca Integrated Systems Laboratory ETH Zurich Switzerland Computer Architecture for Embedded Systems University of Twente Netherlands Department of Industrial Engineering University of Trento Italy Delft University of Technology Department of Space Engineering Netherlands University of Rome Department of Electronic Engineering Tor Vergata Italy Information Engineering at the University of Bologna Department of Electrical Electronic Italy

RISC-V-based fault-tolerant system-on-chip (SoC) designs are critical for the new generation of automotive and space SoC architectures. However, reliability assessment requires characterization under controlled radiation doses to accurately quantify the fault tolerance of the fabricated designs. This work analyzes the Trikarenos design, a SoC implemented in TSMC 28nm, for single event upset (SEU) vulnerability under atmospheric neutron and 200MeV proton radiation, comparing these results to simulation-based fault injection. All faults in error correction codes (ECC) protected memory are corrected by a scrubber, showing an estimated cross-section per bit of up to 1.09 × 10−14 cm2 bit−1. Furthermore, the triple-core lockstep (TCLS) mechanism implemented in Trikarenos is validated and is shown to correct errors affecting a cross-section up to 3.23 × 10−11 cm2, with the remaining uncorrectable vulnerability below 5.36 × 10−12 cm2. When augmenting the experimental analysis of fabricated chips with gate-level fault injection in simulation, 99.10% of injections into the SoC produced correct results, while 100% of injections in the TCLS-protected cores were handled *** 12.28%of all injected faults leading to a TCLS recovery, this indicates an approximate effective flip-flop cross-section of up to 1.28 × 10−14 cm2/FF. © 1963-2012 IEEE.

关键词： Radiation damage

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Evaluating Neighbourhood Roads Through Agent-Based Modelling: A Step Towards the Optimal Pedestrian Desire Path System

SSRN

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SSRN 2024年

作者： Ma, Lei Brandt, S. Anders Seipel, Stefan Ma, Ding Department of Computer and Geospatial Sciences Faculty of Engineering and Sustainable Development University of Gävle Gävle80176 Sweden Division of Visual Information and Interaction Department of Information Technology Uppsala University Uppsala75105 Sweden Research Institute for Smart Cities School of Architecture and Urban Planning Shenzhen University Shenzhen518060 China

As a part of road networks, sidewalks are critical in urban development and people’s lives. This study introduces the Road Evaluation by Desire Path Simulation System (RED-PaSS), an agent-based modelling (ABM) framework designed to assess the alignment of urban sidewalks with pedestrians' natural movement patterns. RED-PaSS simulates pedestrian movement to generate an optimal desire path system, representing the most direct and natural routes that pedestrians would choose in urban settings. The model compares existing sidewalks against these simulated desire paths using key metrics, including road patch distances, segment alignment with desire paths, and origin-destination (OD) flow efficiency. This approach enables urban planners to identify and address gaps in pedestrian infrastructure effectively. In this study, RED-PaSS was applied to evaluate and rank 708 neighbourhood-scale road networks across the United States using high-resolution population density data and open-source road datasets. The evaluation revealed that the sidewalks closely aligned with simulated desire paths offer more direct and efficient pedestrian routes, enhancing natural walking movement. In contrast, poorly aligned sidewalks often force pedestrians to take indirect routes, reducing walkability and efficiency. By identifying discrepancies between existing sidewalks and optimal pedestrian pathways, RED-PaSS provides actionable insights for designing more walkable urban environments and can potentially serve as a valuable tool for urban planners seeking to enhance urban road systems. This work contributes to urban planning by offering a method to improve pedestrian network efficiency, aligning infrastructure with actual pedestrian natural movement behaviours, and emphasizing the need for integrating organic pedestrian movements into formal road network designs to create more functional and walkable cities. © 2024, The Authors. All rights reserved.

关键词： Population statistics

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Artificial neural network-based prediction assessment of wire electric discharge machining parameters for smart manufacturing

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Paladyn 2023年第1期14卷

作者： Manoj, Itagi Vijayakumar Narendranath, SannaYellappa Mashinini, Peter Madindwa Soni, Hargovind Rab, Shanay Ahmad, Shadab Hayat, Ahatsham Department of Mechanical Engineering Nitte Meenakshi Institute of Technology Karnataka Bengaluru560064 India Department of Mechanical Engineering National Institute of Technology Karnataka Surathkal Surathkal575025 India Department of Mechanical & Industrial Engineering Technology University of Johannesburg PO Box 524 Auckland Park2006 South Africa Department of Mechanical Engineering National Institute of Technology Delhi Delhi110036 India School of Architecture Technology and Engineering University of Brighton BrightonBN2 4GJ United Kingdom School of Mechanical Engineering Shandong University of Technology Zibo255000 China Department of Electrical and Computer Engineering University of Nebraska-Lincoln LincolnNE United States

Artificial intelligence (AI), robotics, cybersecurity, the Industrial Internet of Things, and blockchain are some of the technologies and solutions that are combined to produce "smart manufacturing,"which is used to optimize manufacturing processes by creating and/or accepting data. In manufacturing, spark erosion technique such as wire electric discharge machining (WEDM) is a process that machines different hard-to-cut alloys. It is regarded as the solution for cutting intricate parts and materials that are resistant to conventional machining techniques or are required by design. In the present study, holes of different radii, i.e. 1, 3, and 5 mm, have been cut on Nickelvac-HX. Tapering in WEDM is a delicate process to avoid disadvantages such as wire break, wire bend, wire friction, guide wear, and insufficient flushing. Taper angles viz. 0°, 15°, and 30° were obtained from a unique fixture to get holes at different angles. The study also shows the influence of taper angles on the part geometry and area of the holes. Next, the artificial neural network (ANN) technique is implemented for the parametric result prediction. The findings were in good agreement with the experimental data, supporting the viability of the ANN approach for the evaluation of the manufacturing process. The findings in this research provide as a reference to the potential of AI-based assessment in smart manufacturing processes and as a design tool in many manufacturing-related fields. © 2023 the author(s), published by De Gruyter.

关键词： Neural networks

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Enabling VR/AR and Tactile through 5G Network 4

Enabling VR/AR and Tactile through 5G Network

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4th International Conference on Communication, Information and Computing technology, ICCICT 2021

作者： Kusuma, Henry Mathews Shukla, Vinod Kumar Gupta, Shaurya Bachelor of Information Technology Amity University Dubai United Arab Emirates Department of Engineering and Architecture Amity University Dubai United Arab Emirates School of Computer Science University of Petroleum and Energy Studies Uttarakhand Dehradun India

ISBN: (纸本)9781665404303

After the introduction of fifth generation network, there are several prospects and encounters for the evolving technologies like Virtual Reality (VR) and Augmented Reality (AR). This paper emphases on the possibilities of 5G network and how 5G will integrate with Virtual Reality and Augmented Reality and Tactile Internet. Tactile Internet in AR/VR is intended to generate a model alteration from the content-oriented interactions (through internet) towards remote based connections by empowering synchronized broadcast of tactile data like vibration, touch, gestures, surface texture over wireless network with the regular audio and visual data traffics which are also transferred over wireless network. Finally, this research paper intends to provide an overall opinion on the use of Tactile Internet in AR/VR, and to recognize and examine the involved drawbacks and technical problems, and to provide the possible resolutions and to suggest upcoming research guidelines. Initially, opening with the future of Tactile Internet in AR/VR and recent developments in this field, this paper presents a comprehensive outline for Tactile Internet in AR/VR in the past the 5G Era including a Tactile Internet planning, the VR/AR planning, the core technical problems, the important application studies, how it can be possibly enable ground-breaking ways to use Augmented Reality and Virtual Reality. Later, we can see a complete evaluation of the present Tactile Internet progress by generally classifying them into two main instances;specifically, haptic feedback and portable AR/VR device using 5G network. Finally, learning about both Tactile Internet and Augmented Reality/Virtual Reality, we will learn how 5G can change the way Tactile Internet integrates and works with AR/VR that can make an impact in the ever-growing IoT world. © 2021 IEEE.

关键词： 5G mobile communication systems

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Diffusion Unit: Interpretable Edge Enhancement and Suppression Learning for 3D Point Cloud Segmentation

arXiv

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arXiv 2022年

作者： Xiu, Haoyi Liu, Xin Wang, Weimin Kim, Kyoung-Sook Shinohara, Takayuki Chang, Qiong Matsuoka, Masashi Artificial Intelligence Research Center AIST Tokyo Japan Department of Architecture and Building Engineering Tokyo Institute of Technology Tokyo Japan Innovation Technology Office Research Center PASCO Corporation Tokyo Japan Department of Computer Science Tokyo Institute of Technology Tokyo Japan

3D point clouds are discrete samples of continuous surfaces which can be used for various applications. However, the lack of true connectivity information, i.e., edge information, makes point cloud recognition challenging. Recent edge-aware methods incorporate edge modeling into network designs to better describe local structures. Although these methods show that incorporating edge information is beneficial, how edge information helps remains unclear, making it difficult for users to analyze its usefulness. To shed light on this issue, in this study, we propose a new algorithm called Diffusion Unit (DU) that handles edge information in a principled and interpretable manner while providing decent improvement. First, we theoretically show that DU learns to perform task-beneficial edge enhancement and suppression. Second, we experimentally observe and verify the edge enhancement and suppression behavior. Third, we empirically demonstrate that this behavior contributes to performance improvement. Extensive experiments and analyses performed on challenging benchmarks verify the effectiveness of DU. Specifically, our method achieves state-of-the-art performance in object part segmentation using ShapeNet part and scene segmentation using S3DIS. Our source code is available at https://***/martianxiu/DiffusionUnit. © 2022, CC BY.

关键词： Diffusion

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Power Efficient Video Super-Resolution on Mobile NPUs with Deep Learning, Mobile AI & AIM 2022 Challenge: Report

Power Efficient Video Super-Resolution on Mobile NPUs with...

引用

17th European Conference on computer Vision, ECCV 2022

作者： Ignatov, Andrey Timofte, Radu Chiang, Cheng-Ming Kuo, Hsien-Kai Xu, Yu-Syuan Lee, Man-Yu Lu, Allen Cheng, Chia-Ming Chen, Chih-Cheng Yong, Jia-Ying Shuai, Hong-Han Cheng, Wen-Huang Jia, Zhuang Xu, Tianyu Zhang, Yijian Bao, Long Sun, Heng Zhang, Diankai Gao, Si Liu, Shaoli Wu, Biao Zhang, Xiaofeng Zheng, Chengjian Lu, Kaidi Wang, Ning Sun, Xiao Wu, HaoDong Liu, Xuncheng Zhang, Weizhan Yan, Caixia Du, Haipeng Zheng, Qinghua Wang, Qi Chen, Wangdu Duan, Ran Sun, Mengdi Zhu, Dan Chen, Guannan Cho, Hojin Kim, Steve Yue, Shijie Li, Chenghua Zhuge, Zhengyang Chen, Wei Wang, Wenxu Zhou, Yufeng Cai, Xiaochen Cai, Hengxing Xu, Kele Liu, Li Cheng, Zehua Lian, Wenyi Lian, Wenjing Computer Vision Lab ETH Zurich Zürich Switzerland AI Witchlabs Zürich Switzerland University of Wuerzburg Würzburg Germany MediaTek Inc. Hsinchu Taiwan National Yang Ming Chiao Tung University Hsinchu Taiwan Video Algorithm Group Camera Department Xiaomi Inc. Beijing China Audio & Video Technology Platform Department ZTE Corporation Shenzhen China Beijing China School of Computer Science and Technology Xi’an Jiaotong University Xi’an China MIGU Video Co. Ltd. Beijing China BOE Technology Group Co. Ltd. Beijing China GenGenAI Seoul Korea Republic of North China University of Technology Beijing China Institute of Automation Chinese Academy of Sciences Beijing China State Key Laboratory of Computer Architecture Institute of Computing Technology Beijing China 4Paradigm Inc. Beijing China National University of Defense Technology Changsha China University of Oxford Oxford United Kingdom Uppsala University Uppsala Sweden Northeastern University Shenyang China

ISBN: (纸本)9783031250651

Video super-resolution is one of the most popular tasks on mobile devices, being widely used for an automatic improvement of low-bitrate and low-resolution video streams. While numerous solutions have been proposed for this problem, they are usually quite computationally demanding, demonstrating low FPS rates and power efficiency on mobile devices. In this Mobile AI challenge, we address this problem and propose the participants to design an end-to-end real-time video super-resolution solution for mobile NPUs optimized for low energy consumption. The participants were provided with the REDS training dataset containing video sequences for a 4X video upscaling task. The runtime and power efficiency of all models was evaluated on the powerful MediaTek Dimensity 9000 platform with a dedicated AI processing unit capable of accelerating floating-point and quantized neural networks. All proposed solutions are fully compatible with the above NPU, demonstrating an up to 500 FPS rate and 0.2 [Watt/30 FPS] power consumption. A detailed description of all models developed in the challenge is provided in this paper. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Energy utilization

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Comparative Study for Classification Methods to Predict and Detect Epilepsy Seizure 5

Comparative Study for Classification Methods to Predict and ...

引用

5th IEEE Middle East and Africa Conference on Biomedical Engineering, MECBME 2020

作者： El Halabi, Nashaat Abi Zeid Daou, Roy Achkar, Roger Hayek, Ali Boercsoek, Josef American University of Science and Technology Department of Computer and Communications Engineering Faculty of Engineering Beirut Lebanon Faculty of Public Health Biomedical Technologies Department Jounieh Lebanon American University of Science and Technology Department of Computer and Communications Engineering Faculty of Engineering Beirut Lebanon Computer Science University of Kassel Department of Computer Architecture and System Programming Faculty of Electrical Engineering Germany Computer Science University of Kassel Department of Computer Architecture and System Programming Faculty of Electrical Engineering Germany

ISBN: (纸本)9781728123585

Epilepsy is a medical problem that tackles lots of patients. It limits the life activity of such patients due to the seizures that occur anytime and anywhere. Thus, creating a monitoring system that could make their life easier and allow them to perform daily life activities as safe as possible has been studied. This paper is a continuity for a project where a system monitors epileptic patients using a set of wearable sensors connected to several processing units, the main sensors are Electroencephalogram (EEG) for the electrical activity of the brain, electrocardiogram (ECG) for the electrical activity of the heart, and accelerometers to monitor the posture of the patient, features extracted from EEG and ECG signals are used for prediction/detection of seizures. Thus, the objective of this paper is to present a comparative study in order to choose the appropriate classification method for seizure prediction and detection. Five classification methods were studied: Decision Trees, Discriminant Analysis, Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Ensemble Learning. For each of the classification methods, the accuracy, the specificity, the sensitivity, the precision, the false detection rate and the Fmeasure were studied and compared. The results for seizure prediction system show that the highest accuracy, specificity and F-Measure, and lowest false detection rate were achieved with Bagged Trees Ensemble Learning, the highest sensitivity and precision were achieved with Quadratic Support Vector Machine. However, for seizure detection system, the highest accuracy and F-Measure were achieved with Linear Support Vector, the highest sensitivity and specificity were achieved with Fine Gaussian Support Vector Machine, and the highest precision and lowest false detection rate were achieved with Subspace Discriminant Ensemble Learning. © 2020 IEEE.

关键词： Support vector machines

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AUTOMATION OF DISASTER RECOVERY AND SECURITY IN CLOUD COMPUTING 4

AUTOMATION OF DISASTER RECOVERY AND SECURITY IN CLOUD COMPUT...

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4th International Conference on Communication, Information and Computing technology, ICCICT 2021

作者： Kappukalar Nasurudeen, Tahsin Fasila Shukla, Vinod Kumar Gupta, Shaurya Bachelor of Information Technology Amity University Dubai United Arab Emirates Department of Engineering and Architecture Amity University Dubai United Arab Emirates School of Computer Science University of Petroleum and Energy Studies Uttarakhand Dehradun India

ISBN: (纸本)9781665404303

Cloud computing has become a vital part of our everyday life. A decade ago, proposing an idea on the improvement of cloud seemed like a reach. Just like with any other technology, cloud has its own drawbacks too. The way our life is moving at a fast pace, we can’t begin to think of checking our backups every other day. It exhausts most people to even give it a thought. Ransom ware attacks have become so much common which eventually gave rise to the creation of multiple software. Retrieving lost files and data will become much easier if it were to be stored elsewhere prior to an attack. The subject cloud in itself has been overlooked and the fact that the same is the base for further alleviation hasn’t been paid enough attention too. Improving an already existing technology almost always starts with going through its previous version’s capabilities. This paper will mainly focus on how certain techniques are favoured over the other due to it meeting the standards for both personal and official usage. In simpler words, how those said techniques help small businesses develop themselves with the help of dependable backup with low cloud cost. The combination of machine learning and artificial intelligence in automation of disaster recovery testing is the future of cloud computing. When all of this checks the box, security would still be an issue. In this paper, I propose a combined approach to the access of cloud with two major algorithms: Seed block and Rivest Shamir Adleman. ©2021 IEEE

关键词： Automation

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Gender Prediction from Direct Pelvic Radiographs Using Efficientnet Deep Learning Model

SSRN

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SSRN 2024年

作者： Secgin, Yusuf Toy, Seyma Çakmak, Muhammet Oner, Zulal Ciftci, Rukiye Oner, Serkan Karabük University Faculty of Medicine Department of Anatomy Karabük Turkey Sinop University Faculty of Engineering and Architecture Department of Computer Engineering Sinop Turkey İzmir Bakırçay University Faculty of Medicine Department of Anatomy İzmir Turkey Gaziantep Islamic University of Science and Technology Department of Anatomy Gaziantep Turkey İzmir Bakırçay University Faculty of Medicine Department of Radiology İzmir Turkey

Objectives: EfficientNet is an up-to-date deep learning (DL) architecture and an important computer-based algorithm for forensic and basic medical sciences. The aim of this study is to perform highly accurate and precise gender estimation with images obtained from direct pelvic radiographs (X-Ray) using the EfficientNet deep learning *** and Methods: The study was performed using pelvis X-Ray images of 423 individuals aged 18-65 years. For the reliability of the DL architecture, data augmentation was applied to the images and the data set was increased to 1400. EfficientNet V2 B0 architecture was applied to the increased data set. The performance of the architecture was also compared using NASNet Mobile and ResNet152 rooted ***: As a result of the study, a gender prediction rate of 99.29% was obtained with the EfficientNet architecture, 97.86% with the NASNet Mobile architecture and 95.71% with the ResNet152 ***: As a result of the study, EfficientNet V2 B0, NASNet Mobile and ResNet152 DL architectures showed high accuracy and reliability in terms of gender estimation. In this respect, we believe that this study will guide forensic and basic medical sciences. © 2024, The Authors. All rights reserved.

关键词： X ray radiography

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Deploying Fuzzy Rough Set and Artificial Immune System Algorithms for Air Quality Prediction 11th

Deploying Fuzzy Rough Set and Artificial Immune System Algor...

引用

11th International Conference on Robotics, Vision, Signal Processing and Power Applications, RoViSP 2021

作者： Lasisi, Ayodele Ghazali, Rozaida Ismail, Lokman Hakim Husaini, Noor Aida Department of Mathematical Sciences Faculty of Science Augustine University Ilara-Epe Lagos106101 Nigeria Faculty of Computer Science and Information Technology Universiti Tun Hussein Onn Malaysia Batu Pahat Johor Parit Raja86400 Malaysia Department of Design Engineering and Architecture Faculty of Civil and Environmental Engineering Universiti Tun Hussein Onn Malaysia Batu Pahat Johor Parit Raja86400 Malaysia

ISBN: (纸本)9789811681288

The widespread of air pollution due to emissions of highly detrimental concentrates on human life needs paramount attention. Devising air prediction strategy that accurately analyze air quality level through gathering of useful information allow for relevant organizations to disseminate promptly control measures. This paper proposes the use of artificial immune system (AIS) algorithms consisting of Immunos algorithms (Immunos-1, Immunos-2, Immunos-99), CLONal selection ALGorithm (CLONALG), clonal selection classification algorithm (CSCA), artificial immune recognition system algorithms (AIRS1, AIRS2, Parallel AIRS2) for air quality prediction. The fuzzy rough set selects pertinent data features summarizing interpretations of the data. Comparative simulations reveal that the Parallel AIRS2 produced superlative results to other algorithms with detection rate of 96.40%. Effective prediction performance can be generated with AIS algorithms having highest detection rates and lowest false alarm rates. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Forecasting

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