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AMAIX: A Generic Analytical Model for Deep Learning Accelerators 1

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20th international conference on embedded computer systems - architectures, modeling, and simulation (SAMOS)

作者： Juenger, Lukas Zurstrassen, Niko Kogel, Tim Keding, Holger Leupers, Rainer Rhein Westfal TH Aachen Inst Commun Technol & Embedded Syst ICE Aachen Germany Synopsys GmbH Aschheim Germany

ISBN: (数字)9783030609399

ISBN: (纸本)9783030609399;9783030609382

In recent years the growing popularity of Convolutional Neural Networks (CNNs) has driven the development of specialized hardware, so called Deep Learning Accelerators (DLAs). The large market for DLAs and the huge amount of papers published on DLA design show that there is currently no one-size-fits-all solution. Depending on the given optimization goals such as power consumption or performance, there may be several optimal solutions for each scenario. A commonly used method for finding these solutions as early as possible in the design cycle, is the employment of analytical models which try to describe a design by simple yet insightful and sufficiently accurate formulas. The main contribution of this work is the generic Analytical Model for AI accelerators (AMAIX) for the estimation of CNN inference performance on DLAs. It is based on the popular Roofline model. To show the validity of our approach, AMAIX was applied to the Nvidia Deep Learning Accelerator (NVDLA) as a case study using the AlexNet and LeNet CNNs as workloads. The resulting performance predictions were verified against an RTL emulation of the NVDLA using a Synopsys ZeBu Server-based hybrid prototype. AMAIX predicted the inference time of AlexNet and LeNet on the NVDLA with an accuracy of up to 88% and 98% respectively. Furthermore, this work shows how to use the obtained results for root-cause analysis and as a starting point for design space exploration.

关键词： Deep Learning Accelerators Analytical models Design space exploration Roofline model

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MiSim: A Simulator for Resilience Assessment of Microservice-Based architectures

MiSim: A Simulator for Resilience Assessment of Microservice...

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IEEE international conference on Software Quality, Reliability and Security (QRS)

作者： Sebastian Frank Lion Wagner Alireza Hakamian Martin Straesser André van Hoorn University of Hamburg Hamburg Germany University of Stuttgart Stuttgart Germany University of Würzburg Würzburg Germany

Increased resilience compared to monolithic architectures is both one of the key promises of microservice-based architectures and a big challenge, e.g., due to the systems’ distributed nature. Resilience assessment through simulation requires fewer resources than the measurement-based techniques used in practice. However, there is no existing simulation approach that is suitable for a holistic resilience assessment of microservices comprised of (i) representative fault injections, (ii) common resilience mechanisms, and (iii) time-varying workloads. This paper presents MiSim — an extensible simulator for resilience assessment of microservice-based architectures. It overcomes the stated limitations of related work. MiSim fits resilience engineering practices by supporting scenario-based experiments and requiring only lightweight input models. We demonstrate how MiSim simulates (1) common resilience mechanisms — i.e., circuit breaker, connection limiter, retry, load balancer, and autoscaler — and (2) fault injections — i.e., instance/service killing and latency injections. In addition, we use TeaStore, a reference microservice-based architecture, aiming to reproduce scaling behavior from an experiment by using simulation. Our results show that MiSim allows for quantitative insights into microservice-based systems’ complex transient behavior by providing up to 25 metrics.

关键词： Microservice architectures computer architecture Software quality Behavioral sciences Software reliability Circuit faults Integrated circuit modeling

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Design and Development of Drivetrain System for Electric Motorcycles

Design and Development of Drivetrain System for Electric Mot...

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international Middle-East Power System conference, MEPCON

作者： Ahmed R. Alagmy Mohamed M. Sarhan Omar Matar Yahia Elagamy Mohamed H. Khattab Mohamed M. Bekhit Ahmed Mashaly Ahmed Abdelkader Sahar S. Kaddah Basem M. Badr Electrical Engineering Department Faculty of Engineering Mansoura University Mansoura Egypt Mechatronics Engineering Department Faculty of Engineering Mansoura University Mansoura Egypt Renewable and Sustainable Energy Department Faculty of Engineering Mansoura University Mansoura Egypt Production Engineering and Mechanical Design Department Faculty of Engineering Mansoura University Egypt Automotive Engineering Faculty of Engineering Ain Shams University Cairo Egypt Lotus Power Vancouver Canada

ISBN: (数字)9798350379648

ISBN: (纸本)9798350379655

This paper presents the design and analysis of an efficient and low-cost drivetrain system for electric motorcycles (E-Motorcycles). Utilizing advanced CAD (computer-aided design) tools and simulation software such as SolidWorks and MATLAB/SIMULINK, the research evaluates various control strategies for permanent magnet synchronous motors (PMSM) to determine the optimal control scheme. This work focuses on the design and development of a motor drivetrain system that includes an inverter, control systems, PMSM, and drivetrain system. SolidWorks is used to model the CAD of the E-Motorcycle and estimate the inertia of the proposed motor drivetrain system. The CAD analysis and models facilitate and assist the design and development of the motor drive system. A general inverter is designed and built to operate with scalar and vector control schemes for the PMSM, where the control systems are implemented into embedded systems, which are Six-Step, V/F, and Field Oriented Control (FOC) systems. The experiments affirm that the FOC provides the smoothest and most efficient operation, which ensures seamless integration and optimal performance. The integration of the PMSM with the custom-designed inverter demonstrated significant improvements in system efficiency and overall performance.

关键词： Insulated gate bipolar transistors Solid modeling Design automation Motorcycles Pulse width modulation Inverters Mathematical models Software Power systems Machine vector control

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Black-Box Models for Non-Functional Properties of AI Software systems

Black-Box Models for Non-Functional Properties of AI Softwar...

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AI Engineering - Software Engineering for AI (WAIN), IEEE/ACM Workshop on

作者： Daniel Friesel Olaf Spinczyk Universit&#x00E4 t Osnabr&#x00FC ck Osnabr&#x00FC ck Germany

ISBN: (纸本)9781665452069

Non-functional properties (NFPs) such as latency, memory requirements, or hardware cost are an important characteristic of AI software systems, especially in the domain of resource-constrained embedded devices. embedded AI products require sufficient resources for satisfactory latency and accuracy, but should also be cost-efficient and therefore not use more powerful hardware than strictly necessary. Traditionally, modeling and optimization efforts focus on the AI architecture, utilizing methods such as neural architecture search (NAS). However, before developers can start optimizing, they need to know which architectures are suitable candidates for their use case. To this end, architectures must be viewed in context: model post-processing (e.g. quantization), hardware platform, and run-time configuration such as batching all have significant effects on NFPs and therefore on AI architecture performance. Moreover, scalar parameters such as batch size cannot be benchmarked exhaustively. We argue that it is worthwhile to address this issue by means of black-box models before deciding on AI architectures for optimization and hardware/software platforms for inference. To support our claim, we present an AI product line with variable hardware and software components, perform benchmarks, and present notable results. Additionally, we evaluate both compactness and generalization capabilities of regression tree-based modeling approaches from the machine learning and product line engineering communities. We find that linear model trees perform best: they can capture NFPs of known AI configurations with a mean error of up to 13 %, and can predict unseen configurations with a mean error of 10 to 26 %. We find linear model trees to be more compact and interpretable than other tree-based approaches. CCS CONCEPTS • Computing methodologies → modeling methodologies; Machine learning; • Software and its engineering → Extra-functional properties.

关键词： Computational modeling computer architecture Machine learning Predictive models Benchmark testing Software systems Hardware

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An Ensemble Deep Learning Models Based on Metadata for Measuring Arabic Fake News Uncertainty

An Ensemble Deep Learning Models Based on Metadata for Measu...

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international conference on Advanced systems and Electric Technologies (IC_ASET)

作者： Amir Fares Rania Yasmine Chougui Ahlem Drif Silvia Giordano Faculty of sciences University of Setif Algeria Networks and Distributed System Laboratory Faculty of sciences University of Setif 1 Setif Algeria Networking Lab ISIN - DTI University of Applied Sciences of Southern Switzerland - SUPSI Lugano Switzerland

ISBN: (数字)9798350384895

ISBN: (纸本)9798350384901

Fake news, misinformation, and disinformation are significant cybersecurity threats worldwide, as sharing misleading or biased information can have detrimental consequences, leading individuals to act in harmful ways. It is crucial to provide fact-checking tools capable of determining the authenticity of news content, images, and videos. Despite the increasing penetration of fake news in Arabic countries, fake news detection in this context has received limited attention. This work focuses on developing an efficient approach to accurately identify fake news while also measuring the uncertainty of its predictions, a crucial aspect that has been overlooked in the field. We propose a powerful deep ensemble model that incorporates various multi-input merging techniques and different architectures. Our proposed architecture comprises a deep ensemble of three distinct models, each designed to handle specific types of inputs. The ensemble consists of Bi-Long Short Term Memory (Bi-LSTM) model utilizing embedded inputs, a Deep Neural Network (DNN) model incorporating only metadata, and a DNN model employing the Term Frequency-Inverse Document Frequency (TF-IDF) matrix. Additionally, we compute uncertainty of this detection. We further utilize a Bayesian Neural Network (BNN) architecture specifically for metadata to independently measure uncertainty and to give a qualitative evaluation of our uncertainty measure. Experimental results on a challenging dataset demonstrate that our proposed ensemble model outperforms previous works in terms of per-formance. Furthermore, by leveraging the BNN, we showcase how our ensemble architecture surpasses the standalone BNN in uncertainty accuracy. These results not only establish the validity of our proposed ensemble architecture for fake news detection, but also provide valuable information about the uncertainty associated with the model's predictions. By empowering internet users to become more critical consumers of news, these insights

关键词： Uncertainty Accuracy Computational modeling Measurement uncertainty computer architecture Artificial neural networks Predictive models

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Image Classification by Neural Network on Crossbars with Memristor Defects

Image Classification by Neural Network on Crossbars with Mem...

引用

IEEE Region 8 international conference on Computational Technologies in Electrical and Electronics Engineering, SIBIRCON

作者： Mikhail S. Tarkov Evgeniy A. Yatsko Laboratory of Silicon Material Science Rzhanov Institute of Semiconductor Physics SB RAS Novosibirsk Russia Math-Mechanical Faculty Novosibirsk State University Novosibirsk Russia

ISBN: (纸本)9781665464819

Memristor devices are promising in terms of speeding up and improving the power efficiency of deep learning systems. Crossbar architectures built using memristors can be used to efficiently implement various in-memory computational operations such as multiply-accumulate and convolution, which are widely used in deep and convolutional neural networks. The modeling of image processing by neural networks built on the basis of memristor crossbars is performed. The influence of memristor crossbar defects on the quality of image recognition is studied. The proportions of defective memristors for which memristor neural networks are able to correctly classify images are determined. Dependences of the performance of memristor neural networks on their architectures are revealed: the more layers in the network, the stronger the influence of defects in memristors on the network performance. The time of software simulation of a memristor crossbar operation is estimated. The high resistance of memristor crossbars to defects with a large size of crossbars is noted. It has been established that various models of memristors, both linear and non-linear, behave almost identically within convolutional neural networks.

关键词： Resistance Computational modeling Neural networks Memristors computer architecture Production Reliability engineering

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Digital Twin of Piezoelectric Effect at Microscale and Macroscale PVDF Film

Digital Twin of Piezoelectric Effect at Microscale and Macro...

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

作者： Yu-Hsiang Huang Tsung-Han Huang Wei-Song Hung Ching-Yuan Chang Department of Mechanical Engineering National Taipei University of Technology Taipei Taiwan Graduate Institute of Applieed Science and Technology National Taiwan University of Science and Technology Taipei Taiwan

ISBN: (纸本)9781665455718

This study uses computer tomography (CT) and a finite element method (FEM) criterion to establish a digital twin (DT) model for Polyvinylidene fluoride (PVDF) film. The CT provides sliced images of the PVDF film with 50 μm, and we have developed a standard procedure to rebuild the stereo and interior model of the PVDF film. We also use a d33 meter to measure the piezoelectric effect of the PVDF film and utilize the value in the FEM model. The primary material of the piezoelectric film is polyvinylidene fluoride, which has a unique dielectric effect, piezoelectric effect, and pyroelectric effect. In this study, computer tomography was used to establish the microstructure model of the film, and ANSYS software was used to establish the microstructure model. The primary purpose of simulating the piezoelectric effect of the self-made piezoelectric film is to compare the small holes contained in the film structure and the forward and reverse piezoelectric responses of different structures in the same environment. This study has established three different DT-FEM models and simulated the piezoelectric effect of the thin film. The DT-FEM model contributes to the emerging technology of material science and helps scientists to investigate the piezoelectric effect at the micro- and macro-scale.

关键词： Piezoelectric films Solid modeling Three-dimensional displays Computational modeling simulation Piezoelectric effect Software

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Reservoir Computing in Reduced Order modeling for Chaotic Dynamical systems

Reservoir Computing in Reduced Order Modeling for Chaotic Dy...

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35th international ISC High Performance conference

作者： Nogueira Jr, Alberto C. Carvalho, Felipe C. T. Almeida, Joao Lucas S. Codas, Andres Bentivegna, Eloisa Watson, Campbell D. IBM Res Brasil BR-13186900 Hortolandia SP Brazil Univ Estadual Campinas Sch Mech Engn BR-13083860 Campinas SP Brazil IBM Res Europe Warrington WA4 4AD Cheshire England IBM Res Yorktown Hts NY 10598 USA

ISBN: (纸本)9783030905392;9783030905385

The mathematical concept of chaos was introduced by Edward Lorenz in the early 1960s while attempting to represent atmospheric convection through a two-dimensional fluid flow with an imposed temperature difference in the vertical direction. Since then, chaotic dynamical systems are accepted as the foundation of the meteorological sciences and represent an indispensable testbed for weather and climate forecasting tools. Operational weather forecasting platforms rely on costly partial differential equations (PDE)-based models that run continuously on high performance computing architectures. Machine learning (ML)-based low-dimensional surrogate models can be viewed as a cost-effective solution for such high-fidelity simulation platforms. In this work, we propose an ML method based on Reservoir Computing- Echo State Neural Network (RC-ESN) to accurately predict evolutionary states of chaotic systems. We start with the baseline Lorenz-63 and 96 systems and show that RC-ESN is extremely effective in consistently predicting time series using Pearson's cross correlation similarity measure. RC-ESN can accurately forecast Lorenz systems for many Lyapunov time units into the future. In a practical numerical example, we applied RC-ESN combined with space-only proper orthogonal decomposition (POD) to build a reduced order model (ROM) that produces sequential short-term forecasts of pollution dispersion over the continental USA region. We use GEOS-CF simulated data to assess our RC-ESN ROM. Numerical experiments show reasonable results for such a highly complex atmospheric pollution system.

关键词： Machine learning Reservoir computing Reduced order modeling Chaotic systems

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Artificial Neural Network architectures and Orthogonal Arrays in Estimation of Software Projects Efforts Estimation : PLENARY TALK 19

Artificial Neural Network Architectures and Orthogonal Array...

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19th IEEE international Symposium on Intelligent systems and Informatics, SISY 2021

作者： Lazic, Ljubomir UNION University The Computing School Belgrade Serbia

ISBN: (纸本)9781665413800

In the software engineering, estimation of the effort, time and cost required for the development of software projects is an important issue. It is a very difficult task for project managers to predict the cost and effort needed in the premature stages of planning. Software estimation ahead of development can reduce the risk and increase the success rate of the project. Many traditional and machine learning methods are used for software effort estimation by researchers, but always it has been a challenge to predict the effort accurately. In our study, different Artificial Neural Network (ANN) used for effort estimation is discussed. It is observed that the prediction of software effort by using ANN is more precise and better compared to traditional methods such as Function point, Use-case methods and COCOMO etc. Models based on neural networks are competitive in nature as compared to statistical and traditional regression methods. This talk explains the overview of various ANN architectures, and deep learning networks used by the researchers for software effort estimation (SEE). In addition, we have set out own criteria, which has been used to compare all the different selected methods. We have also given a score for each evaluation criteria, so that we can compare the different methods numerically for cost estimation. Our observations have shown that it is best to use a number of different estimating techniques or cost models, and then compare the results before determining the reasons for any of the large variations. None of the methods are necessarily better or worse than the others. We found, in fact, that their strengths and weaknesses often complement each other. Therefore, the main conclusion is that there is no one single technique that is best for every situation, and the results of a number of different approaches need to be carefully considered to discover what is the most likely to produce estimates that are realistic. © 2021 IEEE.

关键词： Neural networks

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A Framework for Multi-level modeling of Analog/Mixed Signal embedded systems 1

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7th international conference on Model-Driven Engineering and Software Development (MODELSWARD)

作者： Genius, Daniela Porto, Rodrigo Cortes Apvrille, Ludovic Pecheux, Francois Sorbonne Univ LIP6 CNRS UMR 7606 Paris France LTCI Telecom Paris Inst Polytech Paris Sophia Antipolis France Tech Univ Kaiserslautern Kaiserslautern Germany

ISBN: (数字)9783030378738

ISBN: (纸本)9783030378738;9783030378721

embedded systems are commonly built upon heterogeneous digital and analog integrated circuits, including sensors and actuators. Model-driven approaches for designing software and hardware are generally limited to the digital parts of systems. In the present paper, we adopt a global view on the extensions made to an integrated modeling and simulation tool, TTool. In this tool, the verification and virtual prototyping of embedded systems is described at different abstraction levels and extended in order to handle analog/mixed-signal systems. An extensive case study spans these levels and illustrates the usefulness of our approach.

关键词： embedded systems

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