Deep Learning And Edge Computing Solutions For High Performance Computing
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Deep Learning And Edge Computing Solutions For High Performance Computing
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Author : A. Suresh
language : en
Publisher: Springer Nature
Release Date : 2021-01-27
Deep Learning And Edge Computing Solutions For High Performance Computing written by A. Suresh and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-01-27 with Technology & Engineering categories.
This book provides an insight into ways of inculcating the need for applying mobile edge data analytics in bioinformatics and medicine. The book is a comprehensive reference that provides an overview of the current state of medical treatments and systems and offers emerging solutions for a more personalized approach to the healthcare field. Topics include deep learning methods for applications in object detection and identification, object tracking, human action recognition, and cross-modal and multimodal data analysis. High performance computing systems for applications in healthcare are also discussed. The contributors also include information on microarray data analysis, sequence analysis, genomics based analytics, disease network analysis, and techniques for big data Analytics and health information technology.
High Performance Computing In Biomimetics
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Author : Kamarul Arifin Ahmad
language : en
Publisher: Springer Nature
Release Date : 2024-03-20
High Performance Computing In Biomimetics written by Kamarul Arifin Ahmad and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-03-20 with Technology & Engineering categories.
This book gives a complete overview of current developments in the implementation of high performance computing (HPC) in various biomimetic technologies. The book presents various topics that are subdivided into the following parts: A) biomimetic models and mechanics; B) locomotion and computational methods; C) distributed computing and its evolution; D) distributed and parallel computing architecture; E) high performance computing and biomimetics; F) big data, management, and visualization; and G) future of high performance computing in biomimetics. This book presents diverse computational technologies to model and replicate biologically inspired design for the purpose of solving complex human problems. The content of this book is presented in a simple and lucid style which can also be used by professionals, non-professionals, scientists, and students who are interested in the research area of high performance computing applications in the development of biomimetics technologies.
Integrating Machine Learning Into Hpc Based Simulations And Analytics
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Author : Ben Youssef, Belgacem
language : en
Publisher: IGI Global
Release Date : 2024-12-13
Integrating Machine Learning Into Hpc Based Simulations And Analytics written by Ben Youssef, Belgacem and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-12-13 with Computers categories.
Researchers are increasingly using machine learning (ML) models to analyze data and simulate complex systems and phenomena. Small-scale computing systems used for training, validation, and testing of these ML models are no longer sufficient for grand-challenge problems characterized by large volumes of data generated at a much higher rate than before, surpassing by far the computing capabilities currently available in many cyberinfrastructure platforms. By associating high-performance computing (HPC) with ML environments, scientists and engineers would be able to enhance not only the scalability but also the performance of their predictive ML models. The Handbook of Research on Integrating Machine Learning Into HPC-Based Simulations and Analytics presents recent research efforts in designing and using ML techniques on HPC systems and discusses some of the results achieved thus far by cutting-edge relevant contributions. Covering topics such as data analytics, deep learning, and networking, this major reference work is ideal for computer scientists, academicians, engineers, researchers, scholars, practitioners, librarians, instructors, and students.
Parallel And High Performance Computing In Artificial Intelligence
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Author : Mukesh Raghuwanshi
language : en
Publisher: CRC Press
Release Date : 2025-05-20
Parallel And High Performance Computing In Artificial Intelligence written by Mukesh Raghuwanshi and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-05-20 with Computers categories.
Parallel and High-Performance Computing in Artificial Intelligence explores high-performance architectures for data-intensive applications as well as efficient analytical strategies to speed up data processing and applications in automation, machine learning, deep learning, healthcare, bioinformatics, natural language processing (NLP), and vision intelligence. The book’s two major themes are high-performance computing (HPC) architecture and techniques and their application in artificial intelligence. Highlights include: HPC use cases, application programming interfaces (APIs), and applications Parallelization techniques HPC for machine learning Implementation of parallel computing with AI in big data analytics HPC with AI in healthcare systems AI in industrial automation Coverage of HPC architecture and techniques includes multicore architectures, parallel-computing techniques, and APIs, as well as dependence analysis for parallel computing. The book also covers hardware acceleration techniques, including those for GPU acceleration to power big data systems. As AI is increasingly being integrated into HPC applications, the book explores emerging and practical applications in such domains as healthcare, agriculture, bioinformatics, and industrial automation. It illustrates technologies and methodologies to boost the velocity and scale of AI analysis for fast discovery. Data scientists and researchers can benefit from the book’s discussion on AI-based HPC applications that can process higher volumes of data, provide more realistic simulations, and guide more accurate predictions. The book also focuses on deep learning and edge computing methodologies with HPC and presents recent research on methodologies and applications of HPC in AI.
Applied Machine Learning And High Performance Computing On Aws
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Author : Mani Khanuja
language : en
Publisher: Packt Publishing Ltd
Release Date : 2022-12-30
Applied Machine Learning And High Performance Computing On Aws written by Mani Khanuja and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-12-30 with Computers categories.
Build, train, and deploy large machine learning models at scale in various domains such as computational fluid dynamics, genomics, autonomous vehicles, and numerical optimization using Amazon SageMaker Key FeaturesUnderstand the need for high-performance computing (HPC)Build, train, and deploy large ML models with billions of parameters using Amazon SageMakerLearn best practices and architectures for implementing ML at scale using HPCBook Description Machine learning (ML) and high-performance computing (HPC) on AWS run compute-intensive workloads across industries and emerging applications. Its use cases can be linked to various verticals, such as computational fluid dynamics (CFD), genomics, and autonomous vehicles. This book provides end-to-end guidance, starting with HPC concepts for storage and networking. It then progresses to working examples on how to process large datasets using SageMaker Studio and EMR. Next, you'll learn how to build, train, and deploy large models using distributed training. Later chapters also guide you through deploying models to edge devices using SageMaker and IoT Greengrass, and performance optimization of ML models, for low latency use cases. By the end of this book, you'll be able to build, train, and deploy your own large-scale ML application, using HPC on AWS, following industry best practices and addressing the key pain points encountered in the application life cycle. What you will learnExplore data management, storage, and fast networking for HPC applicationsFocus on the analysis and visualization of a large volume of data using SparkTrain visual transformer models using SageMaker distributed trainingDeploy and manage ML models at scale on the cloud and at the edgeGet to grips with performance optimization of ML models for low latency workloadsApply HPC to industry domains such as CFD, genomics, AV, and optimizationWho this book is for The book begins with HPC concepts, however, it expects you to have prior machine learning knowledge. This book is for ML engineers and data scientists interested in learning advanced topics on using large datasets for training large models using distributed training concepts on AWS, deploying models at scale, and performance optimization for low latency use cases. Practitioners in fields such as numerical optimization, computation fluid dynamics, autonomous vehicles, and genomics, who require HPC for applying ML models to applications at scale will also find the book useful.
Big Data Machine And Deep Learning
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Author : Rajesh Kumar Mishra
language : en
Publisher: GRIN Verlag
Release Date : 2025-04-11
Big Data Machine And Deep Learning written by Rajesh Kumar Mishra and has been published by GRIN Verlag this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-04-11 with Computers categories.
Scientific Study from the year 2025 in the subject Computer Sciences - Artificial Intelligence, , language: English, abstract: In recent times, developments in artificial intelligence (AI) and machine learning (ML) have propelled improvements in systems and control engineering. We exist in a time of extensive data, where AI and ML can evaluate large volumes of information instantly to enhance efficiency and precision in decisions based on data. In control engineering, for instance, AI algorithms can anticipate system behaviors and autonomously modify controls to enhance performance for better efficiency and dependability. ML models, with their ability to learn, consistently enhance their predictions and choices as they handle additional data, enabling systems to dynamically adjust to evolving environments and operational circumstances. This swift adjustment enhances the functions of current systems and enables the creation of groundbreaking solutions, like self-driving cars and intelligent power grids, which were previously deemed unfeasible. The rapid expansion of digital data has propelled significant advancements in Big Data analytics, Machine Learning, and Deep Learning. These technologies are increasingly integrated across industries, facilitating automated decision-making, predictive modeling, and advanced pattern recognition. This chapter provides an in-depth review of recent progress in these domains, emphasizing breakthroughs in scalable data processing frameworks, cloud and edge computing, AutoML, explainable AI, transformer architectures, self-supervised learning, and generative models. Furthermore, it explores key applications in healthcare, finance, and autonomous systems, along with challenges such as data privacy, ethical concerns, and computational constraints. The discussion concludes with future directions, highlighting the potential of federated learning, neuromorphic computing, and novel algorithmic improvements to further expand AI's impact across disciplines.
Deep Neural Networks For Multimodal Imaging And Biomedical Applications
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Author : Suresh, Annamalai
language : en
Publisher: IGI Global
Release Date : 2020-06-26
Deep Neural Networks For Multimodal Imaging And Biomedical Applications written by Suresh, Annamalai and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-06-26 with Computers categories.
The field of healthcare is seeing a rapid expansion of technological advancement within current medical practices. The implementation of technologies including neural networks, multi-model imaging, genetic algorithms, and soft computing are assisting in predicting and identifying diseases, diagnosing cancer, and the examination of cells. Implementing these biomedical technologies remains a challenge for hospitals worldwide, creating a need for research on the specific applications of these computational techniques. Deep Neural Networks for Multimodal Imaging and Biomedical Applications provides research exploring the theoretical and practical aspects of emerging data computing methods and imaging techniques within healthcare and biomedicine. The publication provides a complete set of information in a single module starting from developing deep neural networks to predicting disease by employing multi-modal imaging. Featuring coverage on a broad range of topics such as prediction models, edge computing, and quantitative measurements, this book is ideally designed for researchers, academicians, physicians, IT consultants, medical software developers, practitioners, policymakers, scholars, and students seeking current research on biomedical advancements and developing computational methods in healthcare.
Deep Learning On Edge Computing Devices
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Author : Xichuan Zhou
language : en
Publisher: Elsevier
Release Date : 2022-02-02
Deep Learning On Edge Computing Devices written by Xichuan Zhou and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-02-02 with Computers categories.
Deep Learning on Edge Computing Devices: Design Challenges of Algorithm and Architecture focuses on hardware architecture and embedded deep learning, including neural networks. The title helps researchers maximize the performance of Edge-deep learning models for mobile computing and other applications by presenting neural network algorithms and hardware design optimization approaches for Edge-deep learning. Applications are introduced in each section, and a comprehensive example, smart surveillance cameras, is presented at the end of the book, integrating innovation in both algorithm and hardware architecture. Structured into three parts, the book covers core concepts, theories and algorithms and architecture optimization.This book provides a solution for researchers looking to maximize the performance of deep learning models on Edge-computing devices through algorithm-hardware co-design. - Focuses on hardware architecture and embedded deep learning, including neural networks - Brings together neural network algorithm and hardware design optimization approaches to deep learning, alongside real-world applications - Considers how Edge computing solves privacy, latency and power consumption concerns related to the use of the Cloud - Describes how to maximize the performance of deep learning on Edge-computing devices - Presents the latest research on neural network compression coding, deep learning algorithms, chip co-design and intelligent monitoring
Edge Intelligence In The Making
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Author : Sen Lin
language : en
Publisher: Springer Nature
Release Date : 2022-06-01
Edge Intelligence In The Making written by Sen Lin and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-06-01 with Computers categories.
With the explosive growth of mobile computing and Internet of Things (IoT) applications, as exemplified by AR/VR, smart city, and video/audio surveillance, billions of mobile and IoT devices are being connected to the Internet, generating zillions of bytes of data at the network edge. Driven by this trend, there is an urgent need to push the frontiers of artificial intelligence (AI) to the network edge to fully unleash the potential of IoT big data. Indeed, the marriage of edge computing and AI has resulted in innovative solutions, namely edge intelligence or edge AI. Nevertheless, research and practice on this emerging inter-disciplinary field is still in its infancy stage. To facilitate the dissemination of the recent advances in edge intelligence in both academia and industry, this book conducts a comprehensive and detailed survey of the recent research efforts and also showcases the authors' own research progress on edge intelligence. Specifically, the book first reviews the background and present motivation for AI running at the network edge. Next, it provides an overview of the overarching architectures, frameworks, and emerging key technologies for deep learning models toward training/inference at the network edge. To illustrate the research problems for edge intelligence, the book also showcases four of the authors' own research projects on edge intelligence, ranging from rigorous theoretical analysis to studies based on realistic implementation. Finally, it discusses the applications, marketplace, and future research opportunities of edge intelligence. This emerging interdisciplinary field offers many open problems and yet also tremendous opportunities, and this book only touches the tip of iceberg. Hopefully, this book will elicit escalating attention, stimulate fruitful discussions, and open new directions on edge intelligence.
Advanced Information Networking And Applications
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Author : Leonard Barolli
language : en
Publisher: Springer Nature
Release Date : 2025-04-22
Advanced Information Networking And Applications written by Leonard Barolli and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-04-22 with Computers categories.
Networks of today are going through a rapid evolution and there are many emerging areas of information networking and their applications. Heterogeneous networking supported by recent technological advances in low power wireless communications along with silicon integration of various functionalities such as sensing, communications, intelligence and actuations are emerging as a critically important disruptive computer class based on a new platform, networking structure and interface that enable novel, low-cost and high-volume applications. Several of such applications have been difficult to realize because of many interconnection problems. To fulfill their large range of applications different kinds of networks need to collaborate and wired and next generation wireless systems should be integrated in order to develop high performance computing solutions to problems arising from the complexities of these networks. This volume covers the theory, design and applications of computer networks, distributed computing and information systems. The aim of the volume “Advanced Information Networking and Applications” is to provide latest research findings, innovative research results, methods and development techniques from both theoretical and practical perspectives related to the emerging areas of information networking and applications.