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TAPE: thermal-aware agent-based power economy for multi/many-core architectures 09

TAPE: thermal-aware agent-based power economy for multi/many...

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Proceedings of the 2009 international conference on computer-Aided Design

作者： Thomas Ebi Mohammad Abdullah Al Faruque Jörg Henkel University of Karlsruhe

ISBN: (纸本)9781605588001

A growing challenge in embedded system design is coping with increasing power densities resulting from packing more and more transistors onto a small die area, which in turn transform into thermal hotspots. In the current late silicon era silicon structures have become more susceptible to transient faults and aging effects resulting from these thermal hotspots. In this paper we present an agent-based power distribution approach (TAPE) which aims to balance the power consumption of a multi/many-core architecture in a pro-active manner. By further taking the system's thermal state into consideration when distributing the power throughout the chip, TAPE is able to noticeably reduce the peak temperature. In our simulation we provide a fair comparison with the state-of-the-art approaches HRTM [19] and PDTM [9] using the MiBench benchmark suite [18]. When running multiple applications simultaneously on a multi/many-core architecture, we are able to achieve an 11.23% decrease in peak temperature compared to the approach that uses no thermal management [14]. At the same time we reduce the execution time (i.e. we increase the performance of the applications) by 44.2% and reduce the energy consumption by 44.4% compared to PDTM [9]. We also show that our approach exhibits higher scalability, requiring 11.9 times less communication overhead in an architecture with 96 cores compared to the state-of-the-art approaches.

关键词： pro-active algorithm agent-based systems dynamic thermal management multi-core architectures

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GEOS: exascale, multiphysics, open-source simulation for CCS

GEOS: exascale, multiphysics, open-source simulation for CCS

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2024 European conference on the Mathematics of Geological Reservoirs, ECMOR 2024

作者： Gross, H. Settgast, R.R. Aronson, R.M. Borio, A. Bui, Q.M. Castelletto, N. Corbett, B.C. Cordier, P. Cremon, M.A. Cusini, M. Frambati, S. Franc, J. Gazzola, T. Hamon, F. Huang, J. Kachuma, D. Karimi-Fard, M. Klevtsov, S. Lapene, A. Magri, V.A.P. Mazuyer, A. N'diaye, M. Osei-Kuffuor, D. Sherman, C.S. Tchelepi, H.A. Tobin, W.R. Tomin, P. Wen, X. White, J.A. TotalEnergies EP Research & Technology USA United States Lawrence Livermore National Laboratory United States Politecnico di Torino Italy Stanford University United States Chevron Inc.

ISBN: (纸本)9798331313319

CO2 capture and sequestration (CCS) aims to reduce the concentration of carbon in our atmosphere. The international Energy Agency predicts that CCS will need to climb from the current injection of 40MtCO2/year to 1.2GtCO2/year by 2030 for a 2050 Net-Zero Scenario. Fast and accurate simulations are required to plan CCS developments and reduce operational risks. Existing commercial simulators do not suffice: the impact of multiple coupled physical phenomena, the century timescale, and basin-sized models in CCS operations force us to completely take apart and revisit the numerical backbone of subsurface simulation and to build reservoir simulation capabilities tailored to the physical complexity of CCS. Because of the numerical challenges in multiphysics coupled systems, this tool needs to be optimized for mathematical performance and computational portability on advanced computing architectures. Finally, this tool targets applications where private entities, universities, and regulators must work together. It must be accessible to simple computer systems, be open-source, and fully auditable. Here, we present GEOS, an open-source, multiphysics, exascale subsurface simulator. Since 2018, GEOS has been developed as a partnership between a US National Laboratory, university, and private institutions. GEOS solves multiphysics simulations with fully coupled geomechanics and flow using a combination of finite volume and finite element discretization. GEOS leverages scalable linear solvers such as Hypre and preconditioners such as Multigrid reduction. It uses open-source portability layers RAJA and CHAI, guaranteeing scalability on various high-performance computing systems. GEOS is coded in C++17. Finally, because it is open-source and freely accessible, GEOS has been extended by other institutions for other multiphysics problems such as thermal flow or wave propagation. In this work, we focus specifically on the numerical design of GEOS from its computational infrastructure

关键词： Scalability

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Adaptive Modulation and Coding in 5G Networks with Deep Learning

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

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Communication, Computing and Signal Processing (IICCCS), IEEE international conference on

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

ISBN: (数字)9798350390759

ISBN: (纸本)9798350390766

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.

关键词： Deep learning Wireless communication Adaptation models Adaptive systems 5G mobile communication Computational modeling Modulation Throughput Encoding Computational efficiency

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Automated validation of operational flight programs (OFPs) and flight training simulators

Automated validation of operational flight programs (OFPs) a...

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IEEE National conference on Aerospace and Electronics (NAECON)

作者： J. Van Fleet S. Flannery J. Rura Science Applications International Corporation USA

Operational flight programs (OFPs) that run in the embedded computer systems (ECSs) in modern aircraft must undergo extensive and rigorous validation testing before they are released for flight. Similar validation testing is required for the highest fidelity flight training simulators to assure that they accurately represent the behavior of the actual aircraft. This validation process, today, is largely manual. Test engineers develop and execute the test procedures by hand an the avionics test station or flight simulator. Manual testing is very time intensive, prone to errors, difficult to repeat precisely, and requires highly qualified experts. The automated validation (AutoVal) tool suite, developed by the Avionics Logistics Branch at Wright Laboratory (WL/AAAF), offers an alternative approach to testing that addresses these problems. AutoVal performs real-time testing of OFP and flight simulator software by completely automating the actions of the test engineers. It utilizes a testing-oriented command language that features user-definable macros for tailoring commands to specific applications. AutoVal reads and interprets commands stored in files or entered interactively through a command-line interface. Test engineers can generate reusable command files that configure and initialize the simulation, control the simulation flight dynamics and avionics modes, verify test results, and automatically create test logs. The most recent AutoVal research has focused on two areas: citation of an AutoVal translator that will produce MIL-STD-1815A Ada source code; and development of a learning capability that will record a test engineers actions and generate AutoVal command files.< >

关键词： Automatic testing Aerospace simulation Aerospace electronics Aerospace engineering Aircraft propulsion Software testing embedded computing System testing Computational modeling Logistics

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Source Code Annotated Memory Leak Detection for Soft Real Time embedded systems with Resource Constraints

Source Code Annotated Memory Leak Detection for Soft Real Ti...

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IEEE international Symposium on Dependable, Autonomic and Secure Computing (DASC)

作者： Mabel Mary Joy Wolfgang Mueller Franz Josef Rammig C-Lab University of Paderborn Germany Heinz Nixdorf Institute University of Paderborn Germany

With ever increasing complexity of software and increased competition in the market, it is very important to minimize the production to market delivery time. Early phase detection of serious errors in the design and program can be done with automated approaches which save time. Memory leaks are the most important memory related problems which are severe threats to software applications. We propose a novel hybrid automated source code annotated memory leak detection approach for soft real time embedded system software with no garbage collection. We utilize our source code annotation tool to detect the potential memory leak candidates, which are further analyzed in our hybrid leak detection approach. The control flow graph of the source code generated with our annotation tool provides information of all execution paths, where the basic blocks are annotated with specific marks. Our hybrid method combines static and dynamic approaches. Static phase of the hybrid approach detect the potential leak candidates from the control flow graph. Dynamic phase filters false positives from the static phase results and provide results on the actual leaks. In early design stages where a real execution environment and complete executable software are unavailable, a simulation environment and a virtual platform are necessary. Virtual platforms provide flexibility to change the target architecture to be tested. Our proposed dynamic phase of the approach runs on the virtual memory model platform we implemented in SystemC at an abstract level using Transaction Level modeling. The software under test is run on top of this model. This makes our approach faster and provides early results. Our approach can be easily deployed across a variety of architectures as it is compiler independent and does not implement any architecture specific features. Moreover the automated approach provides an efficient methodology to determine potential leaks in early phases of software development.

关键词： Leak detection Software Memory management Resource management Real-time systems Pattern matching

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IoT Enabled Early Detection of Lung Cancer Using CT Scan Images and Machine Learning Techniques

IoT Enabled Early Detection of Lung Cancer Using CT Scan Ima...

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international conference on Signal Processing, Communication, Power and embedded System (SCOPES)

作者： Satya Brata Rath A.V.S Pavan Kumar Vishal Kumar Swain Neelamadhab Padhy Rasmita Panigrahi Tirupati Sahu Department of CSE Gandhi Institute of Engineering and Technology University Gunupur India

ISBN: (数字)9798331506452

ISBN: (纸本)9798331506469

Context: The IoT sensors can play a crucial role in enhancing CT scan lung detection, which will help monitor patient vital signs and improve patient comfort. In this disease the lung grows out of control, in recent years, many systems have been developed for the diagnosis of various diseases, among which the early detection of lung cancer is critical for which early detection is crucial for improving treatment outcomes. Objective: The objective is to develop and evaluate deep learning models for lung cancer detection using CT scans (Computed tomography scans). Method and Materials: We trained and tested different architectures of convolutional neural network (CNN) models, including VGG16, ResNet50, Inception-v3, DenseN et121, and an ensemble model combining DenseNet121 + Inception-v3, using a dataset of CT scans. Dataset Descriptions: We have taken an existing dataset of CT scans from the Kaggle repository which contains the different types of chest cancer. The images are in the form of JPGs to fit in the model. The dataset includes 3 chest cancer types large cell carcinoma, Adenocarcinoma, Squamous cell carcinoma, and normal cell. Results: In this experiment research the ensemble (DenseNet121 + Inception - v3) model achieved the highest accuracy 98.25% training and 99.4% testing.

关键词： Deep learning Computed tomography Lungs Computational modeling Microprocessors Lung cancer computer architecture Feature extraction Convolutional neural networks Monitoring

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Developments in Control Engineering Education: Lab-in-a-Box Project Based Learning

Developments in Control Engineering Education: Lab-in-a-Box ...

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UKACC international conference on Control (CONTROL)

作者： James E. Pickering Department of Mechanical Biomedical and Design Engineering Aston University Birmingham UK

ISBN: (数字)9798350374261

ISBN: (纸本)9798350374278

The teaching of autonomous vehicle control algorithm design involves the drawing together of multiple topics from both theory and practice. To enable an in-depth knowledge of how the control algorithms of an AV operate, a laboratory-scale approach using ‘lab-in-a-box’ has been developed at Aston University for a project-based module on the Future Vehicle Technologies MSc Course. The aim of the approach is such that students can learn the control engineering fundamentals before moving onto a larger vehicle platform. The adopted approach borrows ideas inspired from model-based design for embedded control. Continuous-time and discrete-time simulation tools are used to design what is in effect a real-time control system. Students are also taught ‘model shop’ skills and computer aided design (CAD), thus allowing the students to build a physical control system to operate in real-time. The focus is on the control of a DC motor, including speed control, temperature control and obstacle avoidance. Initial results presented here are promising, leading to increased levels of student satisfaction and engagement.

关键词： Solid modeling Design automation Education Velocity control DC motors Control engineering education Real-time systems

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Simple Cloud Federation

Simple Cloud Federation

引用

Asia international conference on modeling & simulation

作者： Dinkar Sitaram H.L. Phalachandra Sudheendra Harwalkar Sirushti Murugesan Pavan Sudheendra Rohith Ananth B. Vidhisha Abdul Hannan Kanji Swati Chidambar Bhat B Kruti PES Institute of Technology Bangalore India PES University Bangalore Karnataka IN

Hybrid clouds help in establishing a cost effective, reliable and scalable solution to limitations in traditional on-premise data-center. There is an increasing requirement for federated hybrid cloud solutions to allow private clouds to leverage different public cloud providers. Federated cloud solutions should accommodate infrastructure constraints, balance load between different clouds and keep the cost at a minimal for the client. The following paper proposes an architecture for hybrid clouds called Simple Cloud Federation (SCF). It differs from existing architectures in that it leverages the hierarchical organization features already found in existing cloud systems. It is thus general and easy to implement across a variety of cloud systems, including OpenStack and Amazon. The paper contains details of our OpenStack implementation.

关键词： Cloud computing computer architecture Virtual machining Security Schedules Filtering algorithms Organizations

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DL Based System for On-Board Image Classification in Real Time, Applied to Disaster Mitigation

DL Based System for On-Board Image Classification in Real Ti...

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international conference on Parallel Distributed and Grid Computing (PDGC)

作者： Amogh Shukla Shubh Almal Ankur Gupta Rinisha Jain Rishabh Mishra Dharmesh Dhabliya School of CSE Vellore Institute of Technology Vellore TL India Department of CSE Vaish College of Engineering Rohtak Haryana India Vishwakarma Institute of Information Technology Pune Maharashtra India

ISBN: (纸本)9781665454025

Natural phenomena such as earthquakes that cause the destruction of bridges and can cause tsunamis, among other phenomena and disasters, are on the rise as a result of climate change, which is affecting our planet. This is because of the rise in average global temperature and the development of extreme rains, which causes rivers to overflow. The use of satellites for terrestrial observation has made it possible to keep tabs on events throughout the world in recent years. However, these satellite systems aren't without their drawbacks; one is that cloud cover may make it difficult to accurately record photographs. It is done based on the clouds present, and another drawback is the visiting time that the satellites have, which means that the area that you want to record the image is not always available, so you have to wait for the day and the time in which the satellite orbit can pass through the area of interest that the image is to be recorded. Drones, which fly at low altitudes and do not have cloud problems, are one such alternative, solving a problem of satellites, on cloud cover. In light of this, in the present work, a methodology based on low-cost systems is proposed for on-board processing in drones of such data in the event of an emergency, such as after the effects of rain or an earthquake. Methodology involves processing the image in an embedded device, which is coupled to the drone and connected to the camera, in such a way that the information that is sent to the ground station, concentrates on the affected area; this allows for faster decision-making and lessens the impact of the natural disaster. Therefore, it is shown by testing of the embedded computer based on a Raspberry pi 3 for detecting changes in land cover and transmitting this information to an earth station.

关键词： Training Satellites Rain Clouds Computational modeling simulation Earthquakes

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Digital Learning Ecosystem: Online Concept for Engineering Education

Digital Learning Ecosystem: Online Concept for Engineering E...

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international Scientific conference on Power and Electrical Engineering of Riga Technical University (RTUCON)

作者： Anatolijs Zabasta Volodymyr Kazymyr Nadezhda Kunicina Oleksandr Velihorskyi Faculty of Computer Science Information Technology and Energy Riga Technical University Riga Latvia Information and Computer Systems Department Chernihiv Polytechnic National University Chernihiv Ukraine Radio Engineering and Embedded Systems Department Chernihiv Polytechnic National University Chernihiv Ukraine

ISBN: (数字)9798350365771

ISBN: (纸本)9798350365788

This article examines the development of a Digital Learning Ecosystem (DLE) for engineering education, supported by online learning, within the framework of Erasmus+ international educational projects. We hypothesize that a DLE can address the challenges posed by rapid technological changes associated with Industry 4.0 and digital transformation, thus transforming engineering education to meet modern industry needs. Building on the earlier Erasmus+ CybPhys project, which established a Sharing Modelling and simulation Environment (SMSE) for digitalizing online technical courses, the DIGITRANS project aims to enhance this architecture to include remote laboratories through the Sharing Remote Experiment Environment (SREE) platform. The project involves acquiring hardware and open-source software for SREE and establishing two physical remote laboratories. A study conducted in spring 2023, using the Technology Acceptance Model framework, indicated positive student perceptions of SMSE, with students believing it would enhance learning outcomes and expressing intentions to use the platform frequently. The efficacy of the DLE in addressing technological advancement challenges will be further evaluated through post-ante assessments and feedback from prospective employers and stakeholders, focusing on the implementation of industry recommendations and the correlation between pre- and post-implementation survey responses from industry and academic experts.

关键词： Training Surveys Remote laboratories Biological system modeling Ecosystems Hardware Stakeholders Engineering education Kernel Programming profession

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