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From ancient flint hand daggers to the futuristic M1A2 tanks of today, flip through a series of stunning visuals to discover the weapons and vehicles that have shaped the military world. With rich illustrations, striking photography, and inputs from experts, Machines of War presents the story of all forms of weaponry that have dominated the battlefield, right from the pre-industrial age to the 21st century. Get a close-up look at firearms, aircraft, tanks, warships, and learn about the invention, evolution, and progression of arms and armaments through the ages. Presenting weapons and vehicles in innovative detail, this one-of-a-kind reference book offers a unique perspective on military developments in the Industrial era, World War I, World War II, the Cold War, and the modern world. Readers will uncover intriguing aspects of the Gatling gun, the Spitfire fighter plane, the T-72 Tank and many more with virtual tours. Whether you're a history lover or a science buff, Machines of War is guaranteed to enthral you by putting you at the helm of war's most formidable weapons.
This book proposes probabilistic machine learning models that represent the hardware properties of the device hosting them. These models can be used to evaluate the impact that a specific device configuration may have on resource consumption and performance of the machine learning task, with the overarching goal of balancing the two optimally. The book first motivates extreme-edge computing in the context of the Internet of Things (IoT) paradigm. Then, it briefly reviews the steps involved in the execution of a machine learning task and identifies the implications associated with implementing this type of workload in resource-constrained devices. The core of this book focuses on augmenting and exploiting the properties of Bayesian Networks and Probabilistic Circuits in order to endow them with hardware-awareness. The proposed models can encode the properties of various device sub-systems that are typically not considered by other resource-aware strategies, bringing about resource-saving opportunities that traditional approaches fail to uncover. The performance of the proposed models and strategies is empirically evaluated for several use cases. All of the considered examples show the potential of attaining significant resource-saving opportunities with minimal accuracy losses at application time. Overall, this book constitutes a novel approach to hardware-algorithm co-optimization that further bridges the fields of Machine Learning and Electrical Engineering.
Machine learning is a potential solution to resolve bottleneck issues in VLSI via optimizing tasks in the design process. This book aims to provide the latest machine-learning–based methods, algorithms, architectures, and frameworks designed for VLSI design. The focus is on digital, analog, and mixed-signal design techniques, device modeling, physical design, hardware implementation, testability, reconfigurable design, synthesis and verification, and related areas. Chapters include case studies as well as novel research ideas in the given field. Overall, the book provides practical implementations of VLSI design, IC design, and hardware realization using machine learning techniques. Features: Provides the details of state-of-the-art machine learning methods used in VLSI design Discusses hardware implementation and device modeling pertaining to machine learning algorithms Explores machine learning for various VLSI architectures and reconfigurable computing Illustrates the latest techniques for device size and feature optimization Highlights the latest case studies and reviews of the methods used for hardware implementation This book is aimed at researchers, professionals, and graduate students in VLSI, machine learning, electrical and electronic engineering, computer engineering, and hardware systems.