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.

Hardware Accelerator Systems for Artificial Intelligence and Machine Learning, Volume 122 delves into arti?cial Intelligence and the growth it has seen with the advent of Deep Neural Networks (DNNs) and Machine Learning. Updates in this release include chapters on Hardware accelerator systems for artificial intelligence and machine learning, Introduction to Hardware Accelerator Systems for Artificial Intelligence and Machine Learning, Deep Learning with GPUs, Edge Computing Optimization of Deep Learning Models for Specialized Tensor Processing Architectures, Architecture of NPU for DNN, Hardware Architecture for Convolutional Neural Network for Image Processing, FPGA based Neural Network Accelerators, and much more. Updates on new information on the architecture of GPU, NPU and DNN Discusses In-memory computing, Machine intelligence and Quantum computing Includes sections on Hardware Accelerator Systems to improve processing efficiency and performance

This book explores new methods, architectures, tools, and algorithms for Artificial Intelligence Hardware Accelerators. The authors have structured the material to simplify readers’ journey toward understanding the aspects of designing hardware accelerators, complex AI algorithms, and their computational requirements, along with the multifaceted applications. Coverage focuses broadly on the hardware aspects of training, inference, mobile devices, and autonomous vehicles (AVs) based AI accelerators

The information infrastructure – comprising computers, embedded devices, networks and software systems – is vital to operations in every sector: chemicals, commercial facilities, communications, critical manufacturing, dams, defense industrial base, emergency services, energy, financial services, food and agriculture, government facilities, healthcare and public health, information technology, nuclear reactors, materials and waste, transportation systems, and water and wastewater systems. Global business and industry, governments, indeed society itself, cannot function if major components of the critical information infrastructure are degraded, disabled or destroyed. Critical Infrastructure Protection XVI describes original research results and innovative applications in the interdisciplinary field of critical infrastructure protection. Also, it highlights the importance of weaving science, technology and policy in crafting sophisticated, yet practical, solutions that will help secure information, computer and network assets in the various critical infrastructure sectors. Areas of coverage include: Industrial Control Systems Security; Telecommunications Systems Security; Infrastructure Security. This book is the 16th volume in the annual series produced by the International Federation for Information Processing (IFIP) Working Group 11.10 on Critical Infrastructure Protection, an international community of scientists, engineers, practitioners and policy makers dedicated to advancing research, development and implementation efforts focused on infrastructure protection. The book contains a selection of 11 edited papers from the Fifteenth Annual IFIP WG 11.10 International Conference on Critical Infrastructure Protection, held as a virtual event during March, 2022. Critical Infrastructure Protection XVI is an important resource for researchers, faculty members and graduate students, as well as for policy makers, practitioners and other individuals with interests in homeland security.

Craft ethical AI projects with privacy, fairness, and risk assessment features for scalable and distributed systems while maintaining explainability and sustainability Purchase of the print or Kindle book includes a free PDF eBook Key Features Learn risk assessment for machine learning frameworks in a global landscape Discover patterns for next-generation AI ecosystems for successful product design Make explainable predictions for privacy and fairness-enabled ML training Book Description AI algorithms are ubiquitous and used for tasks, from recruiting to deciding who will get a loan. With such widespread use of AI in the decision-making process, it's necessary to build an explainable, responsible, transparent, and trustworthy AI-enabled system. With Platform and Model Design for Responsible AI, you'll be able to make existing black box models transparent. You'll be able to identify and eliminate bias in your models, deal with uncertainty arising from both data and model limitations, and provide a responsible AI solution. You'll start by designing ethical models for traditional and deep learning ML models, as well as deploying them in a sustainable production setup. After that, you'll learn how to set up data pipelines, validate datasets, and set up component microservices in a secure and private way in any cloud-agnostic framework. You'll then build a fair and private ML model with proper constraints, tune the hyperparameters, and evaluate the model metrics. By the end of this book, you'll know the best practices to comply with data privacy and ethics laws, in addition to the techniques needed for data anonymization. You'll be able to develop models with explainability, store them in feature stores, and handle uncertainty in model predictions. What you will learn Understand the threats and risks involved in ML models Discover varying levels of risk mitigation strategies and risk tiering tools Apply traditional and deep learning optimization techniques efficiently Build auditable and interpretable ML models and feature stores Understand the concept of uncertainty and explore model explainability tools Develop models for different clouds including AWS, Azure, and GCP Explore ML orchestration tools such as Kubeflow and Vertex AI Incorporate privacy and fairness in ML models from design to deployment Who this book is for This book is for experienced machine learning professionals looking to understand the risks and leakages of ML models and frameworks, and learn to develop and use reusable components to reduce effort and cost in setting up and maintaining the AI ecosystem.

Hardware acceleration in the form of customized datapath and control circuitry tuned to specific applications has gained popularity for its promise to utilize transistors more efficiently. Historically, the computer architecture community has focused on general-purpose processors, and extensive research infrastructure has been developed to support research efforts in this domain. Envisioning future computing systems with a diverse set of general-purpose cores and accelerators, computer architects must add accelerator-related research infrastructures to their toolboxes to explore future heterogeneous systems. This book serves as a primer for the field, as an overview of the vast literature on accelerator architectures and their design flows, and as a resource guidebook for researchers working in related areas.

This book constitutes the proceedings of the 15th International Symposium on Applied Reconfigurable Computing, ARC 2019, held in Darmstadt, Germany, in April 2019. The 20 full papers and 7 short papers presented in this volume were carefully reviewed and selected from 52 submissions. In addition, the volume contains 1 invited paper. The papers were organized in topical sections named: Applications; partial reconfiguration and security; image/video processing; high-level synthesis; CGRAs and vector processing; architectures; design frameworks and methodology; convolutional neural networks.