High Performance Embedded Computing Handbook A Systems Perspective


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High Performance Embedded Computing Handbook


High Performance Embedded Computing Handbook

Author: David R. Martinez

language: en

Publisher: CRC Press

Release Date: 2018-10-03


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Over the past several decades, applications permeated by advances in digital signal processing have undergone unprecedented growth in capabilities. The editors and authors of High Performance Embedded Computing Handbook: A Systems Perspective have been significant contributors to this field, and the principles and techniques presented in the handbook are reinforced by examples drawn from their work. The chapters cover system components found in today’s HPEC systems by addressing design trade-offs, implementation options, and techniques of the trade, then solidifying the concepts with specific HPEC system examples. This approach provides a more valuable learning tool, Because readers learn about these subject areas through factual implementation cases drawn from the contributing authors’ own experiences. Discussions include: Key subsystems and components Computational characteristics of high performance embedded algorithms and applications Front-end real-time processor technologies such as analog-to-digital conversion, application-specific integrated circuits, field programmable gate arrays, and intellectual property–based design Programmable HPEC systems technology, including interconnection fabrics, parallel and distributed processing, performance metrics and software architecture, and automatic code parallelization and optimization Examples of complex HPEC systems representative of actual prototype developments Application examples, including radar, communications, electro-optical, and sonar applications The handbook is organized around a canonical framework that helps readers navigate through the chapters, and it concludes with a discussion of future trends in HPEC systems. The material is covered at a level suitable for practicing engineers and HPEC computational practitioners and is easily adaptable to their own implementation requirements.

Verification, Validation, and Testing of Engineered Systems


Verification, Validation, and Testing of Engineered Systems

Author: Avner Engel

language: en

Publisher: John Wiley & Sons

Release Date: 2010-06-15


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Systems' Verification Validation and Testing (VVT) are carried out throughout systems' lifetimes. Notably, quality-cost expended on performing VVT activities and correcting system defects consumes about half of the overall engineering cost. Verification, Validation and Testing of Engineered Systems provides a comprehensive compendium of VVT activities and corresponding VVT methods for implementation throughout the entire lifecycle of an engineered system. In addition, the book strives to alleviate the fundamental testing conundrum, namely: What should be tested? How should one test? When should one test? And, when should one stop testing? In other words, how should one select a VVT strategy and how it be optimized? The book is organized in three parts: The first part provides introductory material about systems and VVT concepts. This part presents a comprehensive explanation of the role of VVT in the process of engineered systems (Chapter-1). The second part describes 40 systems' development VVT activities (Chapter-2) and 27 systems' post-development activities (Chapter-3). Corresponding to these activities, this part also describes 17 non-testing systems' VVT methods (Chapter-4) and 33 testing systems' methods (Chapter-5). The third part of the book describes ways to model systems' quality cost, time and risk (Chapter-6), as well as ways to acquire quality data and optimize the VVT strategy in the face of funding, time and other resource limitations as well as different business objectives (Chapter-7). Finally, this part describes the methodology used to validate the quality model along with a case study describing a system's quality improvements (Chapter-8). Fundamentally, this book is written with two categories of audience in mind. The first category is composed of VVT practitioners, including Systems, Test, Production and Maintenance engineers as well as first and second line managers. The second category is composed of students and faculties of Systems, Electrical, Aerospace, Mechanical and Industrial Engineering schools. This book may be fully covered in two to three graduate level semesters; although parts of the book may be covered in one semester. University instructors will most likely use the book to provide engineering students with knowledge about VVT, as well as to give students an introduction to formal modeling and optimization of VVT strategy.

Artificial Intelligence


Artificial Intelligence

Author: David R. Martinez

language: en

Publisher: MIT Press

Release Date: 2024-06-11


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The first text to take a systems engineering approach to artificial intelligence (AI), from architecture principles to the development and deployment of AI capabilities. Most books on artificial intelligence (AI) focus on a single functional building block, such as machine learning or human-machine teaming. Artificial Intelligence takes a more holistic approach, addressing AI from the view of systems engineering. The book centers on the people-process-technology triad that is critical to successful development of AI products and services. Development starts with an AI design, based on the AI system architecture, and culminates with successful deployment of the AI capabilities. Directed toward AI developers and operational users, this accessibly written volume of the MIT Lincoln Laboratory Series can also serve as a text for undergraduate seniors and graduate-level students and as a reference book. Key features: In-depth look at modern computing technologies Systems engineering description and means to successfully undertake an AI product or service development through deployment Existing methods for applying machine learning operations (MLOps) AI system architecture including a description of each of the AI pipeline building blocks Challenges and approaches to attend to responsible AI in practice Tools to develop a strategic roadmap and techniques to foster an innovative team environment Multiple use cases that stem from the authors’ MIT classes, as well as from AI practitioners, AI project managers, early-career AI team leaders, technical executives, and entrepreneurs Exercises and Jupyter notebook examples


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