Proceedings of the 2017 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays PDF Download
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Author: Jonathan Greene
Publisher:
ISBN: 9781450343541
Category :
Languages : en
Pages :
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Book Description
FPGA '17: The 2017 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays Feb 22, 2017-Feb 24, 2017 Monterey, USA. You can view more information about this proceeding and all of ACM�s other published conference proceedings from the ACM Digital Library: http://www.acm.org/dl.
Author: Jonathan Greene
Publisher:
ISBN: 9781450343541
Category :
Languages : en
Pages :
Get Book Here
Book Description
FPGA '17: The 2017 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays Feb 22, 2017-Feb 24, 2017 Monterey, USA. You can view more information about this proceeding and all of ACM�s other published conference proceedings from the ACM Digital Library: http://www.acm.org/dl.
Author: Qi Liu
Publisher: Springer Nature
ISBN: 9811969019
Category : Technology & Engineering
Languages : en
Pages : 1506
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Book Description
This conference proceeding is a collection of the papers accepted by the CENet2022 – the 12th International Conference on Computer Engineering and Networks held on November 4-7, 2022 in Haikou, China. The topics focus but are not limited to Internet of Things and Smart Systems, Artificial Intelligence and Applications, Communication System Detection, Analysis and Application, and Medical Engineering and Information Systems. Each part can be used as an excellent reference by industry practitioners, university faculties, research fellows and undergraduates as well as graduate students who need to build a knowledge base of the most current advances and state-of-practice in the topics covered by this conference proceedings. This will enable them to produce, maintain, and manage systems with high levels of trustworthiness and complexity.
Author: Xin-She Yang
Publisher: Springer Nature
ISBN: 9819732999
Category :
Languages : en
Pages : 635
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Book Description
Author: Christoforos Kachris
Publisher: Springer
ISBN: 3319927922
Category : Technology & Engineering
Languages : en
Pages : 280
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Book Description
This book provides readers with an overview of the architectures, programming frameworks, and hardware accelerators for typical cloud computing applications in data centers. The authors present the most recent and promising solutions, using hardware accelerators to provide high throughput, reduced latency and higher energy efficiency compared to current servers based on commodity processors. Readers will benefit from state-of-the-art information regarding application requirements in contemporary data centers, computational complexity of typical tasks in cloud computing, and a programming framework for the efficient utilization of the hardware accelerators.
Author: Vivienne Sze
Publisher: Springer Nature
ISBN: 3031017668
Category : Technology & Engineering
Languages : en
Pages : 254
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Book Description
This book provides a structured treatment of the key principles and techniques for enabling efficient processing of deep neural networks (DNNs). DNNs are currently widely used for many artificial intelligence (AI) applications, including computer vision, speech recognition, and robotics. While DNNs deliver state-of-the-art accuracy on many AI tasks, it comes at the cost of high computational complexity. Therefore, techniques that enable efficient processing of deep neural networks to improve key metrics—such as energy-efficiency, throughput, and latency—without sacrificing accuracy or increasing hardware costs are critical to enabling the wide deployment of DNNs in AI systems. The book includes background on DNN processing; a description and taxonomy of hardware architectural approaches for designing DNN accelerators; key metrics for evaluating and comparing different designs; features of DNN processing that are amenable to hardware/algorithm co-design to improve energy efficiency and throughput; and opportunities for applying new technologies. Readers will find a structured introduction to the field as well as formalization and organization of key concepts from contemporary work that provide insights that may spark new ideas.
Author: Peter Jones
Publisher: Walzone Press
ISBN:
Category : Computers
Languages : en
Pages : 217
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Book Description
Unlock the full potential of deep learning with "Efficient AI Solutions: Deploying Deep Learning with ONNX and CUDA", your comprehensive guide to deploying high-performance AI models across diverse environments. This expertly crafted book navigates the intricate landscape of deep learning deployment, offering in-depth coverage of the pivotal technologies ONNX and CUDA. From optimizing and preparing models for deployment to leveraging accelerated computing for real-time inference, this book equips you with the essential knowledge to bring your deep learning projects to life. Dive into the nuances of model interoperability with ONNX, understand the architecture of CUDA for parallel computing, and explore advanced optimization techniques to enhance model performance. Whether you're deploying to the cloud, edge devices, or mobile platforms, "Efficient AI Solutions: Deploying Deep Learning with ONNX and CUDA" provides strategic insights into cross-platform deployment, ensuring your models achieve broad accessibility and optimal performance. Designed for data scientists, machine learning engineers, and software developers, this resource assumes a foundational understanding of deep learning, guiding readers through a seamless transition from training to production. Troubleshoot with ease and adopt best practices to stay ahead of deployment challenges. Prepare for the future of deep learning deployment with a closer look at emerging trends and technologies shaping the field. Embrace the future of AI with "Efficient AI Solutions: Deploying Deep Learning with ONNX and CUDA" — your pathway to deploying efficient, scalable, and robust deep learning models.
Author: Sawyer D. Campbell
Publisher: John Wiley & Sons
ISBN: 1119853893
Category : Technology & Engineering
Languages : en
Pages : 596
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Book Description
Authoritative reference on the state of the art in the field with additional coverage of important foundational concepts Advances in Electromagnetics Empowered by Artificial Intelligence and Deep Learning presents cutting-edge research advances in the rapidly growing areas in optical and RF electromagnetic device modeling, simulation, and inverse-design. The text provides a comprehensive treatment of the field on subjects ranging from fundamental theoretical principles and new technological developments to state-of-the-art device design, as well as examples encompassing a wide range of related sub-areas. The content of the book covers all-dielectric and metallodielectric optical metasurface deep learning-accelerated inverse-design, deep neural networks for inverse scattering, applications of deep learning for advanced antenna design, and other related topics. To aid in reader comprehension, each chapter contains 10-15 illustrations, including prototype photos, line graphs, and electric field plots. Contributed to by leading research groups in the field, sample topics covered in Advances in Electromagnetics Empowered by Artificial Intelligence and Deep Learning include: Optical and photonic design, including generative machine learning for photonic design and inverse design of electromagnetic systems RF and antenna design, including artificial neural networks for parametric electromagnetic modeling and optimization and analysis of uniform and non-uniform antenna arrays Inverse scattering, target classification, and other applications, including deep learning for high contrast inverse scattering of electrically large structures Advances in Electromagnetics Empowered by Artificial Intelligence and Deep Learning is a must-have resource on the topic for university faculty, graduate students, and engineers within the fields of electromagnetics, wireless communications, antenna/RF design, and photonics, as well as researchers at large defense contractors and government laboratories.
Author: Dino Quintero
Publisher: IBM Redbooks
ISBN: 073845690X
Category : Computers
Languages : en
Pages : 88
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Book Description
This IBM® Redpaper publication provides an update to the original description of IBM Reference Architecture for Genomics. This paper expands the reference architecture to cover all of the major vertical areas of healthcare and life sciences industries, such as genomics, imaging, and clinical and translational research. The architecture was renamed IBM Reference Architecture for High Performance Data and AI in Healthcare and Life Sciences to reflect the fact that it incorporates key building blocks for high-performance computing (HPC) and software-defined storage, and that it supports an expanding infrastructure of leading industry partners, platforms, and frameworks. The reference architecture defines a highly flexible, scalable, and cost-effective platform for accessing, managing, storing, sharing, integrating, and analyzing big data, which can be deployed on-premises, in the cloud, or as a hybrid of the two. IT organizations can use the reference architecture as a high-level guide for overcoming data management challenges and processing bottlenecks that are frequently encountered in personalized healthcare initiatives, and in compute-intensive and data-intensive biomedical workloads. This reference architecture also provides a framework and context for modern healthcare and life sciences institutions to adopt cutting-edge technologies, such as cognitive life sciences solutions, machine learning and deep learning, Spark for analytics, and cloud computing. To illustrate these points, this paper includes case studies describing how clients and IBM Business Partners alike used the reference architecture in the deployments of demanding infrastructures for precision medicine. This publication targets technical professionals (consultants, technical support staff, IT Architects, and IT Specialists) who are responsible for providing life sciences solutions and support.
Author: Saurabh Shrivastava
Publisher: Packt Publishing Ltd
ISBN: 1803244828
Category : Computers
Languages : en
Pages : 693
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Book Description
Become a master Solutions Architect with this comprehensive guide, featuring cloud design patterns and real-world solutions for building scalable, secure, and highly available systems Purchase of the print or Kindle book includes a free eBook in PDF format. Key Features Gain expertise in automating, networking, migrating, and adopting cloud technologies using AWS Use streaming analytics, big data, AI/ML, IoT, quantum computing, and blockchain to transform your business Upskill yourself as an AWS solutions architect and explore details of the new AWS certification Book Description Are you excited to harness the power of AWS and unlock endless possibilities for your business? Look no further than the second edition of AWS for Solutions Architects! Packed with all-new content, this book is a must-have guide for anyone looking to build scalable cloud solutions and drive digital transformation using AWS. This updated edition offers in-depth guidance for building cloud solutions using AWS. It provides detailed information on AWS well-architected design pillars and cloud-native design patterns. You'll learn about networking in AWS, big data and streaming data processing, CloudOps, and emerging technologies such as machine learning, IoT, and blockchain. Additionally, the book includes new sections on storage in AWS, containers with ECS and EKS, and data lake patterns, providing you with valuable insights into designing industry-standard AWS architectures that meet your organization's technological and business requirements. Whether you're an experienced solutions architect or just getting started with AWS, this book has everything you need to confidently build cloud-native workloads and enterprise solutions. What you will learn Optimize your Cloud Workload using the AWS Well-Architected Framework Learn methods to migrate your workload using the AWS Cloud Adoption Framework Apply cloud automation at various layers of application workload to increase efficiency Build a landing zone in AWS and hybrid cloud setups with deep networking techniques Select reference architectures for business scenarios, like data lakes, containers, and serverless apps Apply emerging technologies in your architecture, including AI/ML, IoT and blockchain Who this book is for This book is for application and enterprise architects, developers, and operations engineers who want to become well versed with AWS architectural patterns, best practices, and advanced techniques to build scalable, secure, highly available, highly tolerant, and cost-effective solutions in the cloud. Existing AWS users are bound to learn the most, but it will also help those curious about how leveraging AWS can benefit their organization. Prior knowledge of any computing language is not needed, and there's little to no code. Prior experience in software architecture design will prove helpful.
Author: Martin Schoeberl
Publisher: Springer
ISBN: 3030186563
Category : Computers
Languages : en
Pages : 337
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Book Description
This book constitutes the proceedings of the 32nd International Conference on Architecture of Computing Systems, ARCS 2019, held in Copenhagen, Denmark, in May 2019. The 24 full papers presented in this volume were carefully reviewed and selected from 40 submissions. ARCS has always been a conference attracting leading-edge research outcomes in Computer Architecture and Operating Systems, including a wide spectrum of topics ranging from embedded and real-time systems all the way to large-scale and parallel systems. The selected papers are organized in the following topical sections: Dependable systems; real-time systems; special applications; architecture; memory hierarchy; FPGA; energy awareness; NoC/SoC. The chapter 'MEMPower: Data-Aware GPU Memory Power Model' is open access under a CC BY 4.0 license at link.springer.com.
