Memory Design Techniques for Low Energy Embedded Systems centers one of the most outstanding problems in chip design for embedded application. It guides the reader through different memory organizations and technologies and it reviews the most successful strategies for optimizing them in the power and performance plane.
Industrial applications of evolutionary algorithms" is intended as a resource for both experienced users of evolutionary algorithms and researchers that are beginning to approach these fascinating optimization techniques. Experienced users will find interesting details of real-world problems, advice on solving issues related to fitness computation or modeling, and suggestions on how to set the appropriate parameters to reach optimal solutions. Beginners will find a thorough introduction to evolutionary computation, and a complete presentation of several classes of evolutionary algorithms exploited to solve different problems. Inside, scholars will find useful examples on how to fill the gap between purely theoretical examples and industrial problems. The collection of case studies presented is also extremely appealing for anyone interested in Evolutionary Computation, but without direct access to extensive technical literature on the subject. After the introduction, each chapter in the book presents a test case, and is organized so that it can be read independently from the rest: all the information needed to understand the problem and the approach is reported in each part. Chapters are grouped by three themes of particular interest for real-world applications, namely prototype-based validation, reliability and test generation. The authors hope that this volume will help to expose the flexibility and efficiency of evolutionary techniques, encouraging more companies to adopt them; and that, most of all, you will enjoy your reading.
An embedded system is loosely defined as any system that utilizes electronics but is not perceived or used as a general-purpose computer. Traditionally, one or more electronic circuits or microprocessors are literally embedded in the system, either taking up roles that used to be performed by mechanical devices, or providing functionality that is not otherwise possible. The goal of this book is to investigate how formal methods can be applied to the domain of embedded system design. The emphasis is on the specification, representation, validation, and design exploration of such systems from a high-level perspective. The authors review the framework upon which the theories and experiments are based, and through which the formal methods are linked to synthesis and simulation. A formal verification methodology is formulated to verify general properties of the designs and demonstrate that this methodology is efficient in dealing with the problem of complexity and effective in finding bugs. However, manual intervention in the form of abstraction selection and separation of timing and functionality is required. It is conjectured that, for specific properties, efficient algorithms exist for completely automatic formal validations of systems. Synchronous Equivalence: Formal Methods for Embedded Systems presents a brand new formal approach to high-level equivalence analysis. It opens design exploration avenues previously uncharted. It is a work that can stand alone but at the same time is fully compatible with the synthesis and simulation framework described in another book by Kluwer Academic Publishers Hardware-Software Co-Design of Embedded Systems: The POLIS Approach, by Balarin et al. Synchronous Equivalence: Formal Methods for Embedded Systems will be of interest to embedded system designers (automotive electronics, consumer electronics, and telecommunications), micro-controller designers, CAD developers and students, as well as IP providers, architecture platform designers, operating system providers, and designers of VLSI circuits and systems.
Machine Learning (ML) algorithms have shown a high level of accuracy, and applications are widely used in many systems and platforms. However, developing efficient ML-based systems requires addressing three problems: energy-efficiency, robustness, and techniques that typically focus on optimizing for a single objective/have a limited set of goals. This book tackles these challenges by exploiting the unique features of advanced ML models and investigates cross-layer concepts and techniques to engage both hardware and software-level methods to build robust and energy-efficient architectures for these advanced ML networks. More specifically, this book improves the energy efficiency of complex models like CapsNets, through a specialized flow of hardware-level designs and software-level optimizations exploiting the application-driven knowledge of these systems and the error tolerance through approximations and quantization. This book also improves the robustness of ML models, in particular for SNNs executed on neuromorphic hardware, due to their inherent cost-effective features. This book integrates multiple optimization objectives into specialized frameworks for jointly optimizing the robustness and energy efficiency of these systems. This is an important resource for students and researchers of computer and electrical engineering who are interested in developing energy efficient and robust ML.
The Problem of the Unknown Component: Theory and Applications addresses the issue of designing a component that, combined with a known part of a system, conforms to an overall specification. The authors tackle this problem by solving abstract equations over a language. The most general solutions are studied when both synchronous and parallel composition operators are used. The abstract equations are specialized to languages associated with important classes of automata used for modeling systems. The book is a blend of theory and practice, which includes a description of a software package with applications to sequential synthesis of finite state machines. Specific topologies interconnecting the components, exact and heuristic techniques, and optimization scenarios are studied. Finally the scope is enlarged to domains like testing, supervisory control, game theory and synthesis for special omega languages. The authors present original results of the authors along with an overview of existing ones.
Memory Design Techniques for Low Energy Embedded Systems centers one of the most outstanding problems in chip design for embedded application. It guides the reader through different memory organizations and technologies and it reviews the most successful strategies for optimizing them in the power and performance plane.
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