Author:
Publisher:
ISBN:
Category : Aeronautics
Languages : en
Pages : 1152
Book Description
Scientific and Technical Aerospace Reports
Government Reports Announcements & Index
Government Reports Annual Index
Author:
Publisher:
ISBN:
Category : Government reports announcements & index
Languages : en
Pages : 1236
Book Description
Publisher:
ISBN:
Category : Government reports announcements & index
Languages : en
Pages : 1236
Book Description
Documentation Abstracts
Programmable Integrated Photonics
Author: José Capmany
Publisher:
ISBN: 0198844409
Category : Science
Languages : en
Pages : 361
Book Description
This is the first comprehensive, self-contained introduction to the emergent field of Programmable Integrated Photonics. It covers theoretical and practical aspects ranging from basic technologies and the building of photonic component blocks, to design alternatives and principles of complex programmable photonic circuits, and their applications.
Publisher:
ISBN: 0198844409
Category : Science
Languages : en
Pages : 361
Book Description
This is the first comprehensive, self-contained introduction to the emergent field of Programmable Integrated Photonics. It covers theoretical and practical aspects ranging from basic technologies and the building of photonic component blocks, to design alternatives and principles of complex programmable photonic circuits, and their applications.
TinyML
Author: Pete Warden
Publisher: O'Reilly Media
ISBN: 1492052019
Category : Computers
Languages : en
Pages : 504
Book Description
Deep learning networks are getting smaller. Much smaller. The Google Assistant team can detect words with a model just 14 kilobytes in size—small enough to run on a microcontroller. With this practical book you’ll enter the field of TinyML, where deep learning and embedded systems combine to make astounding things possible with tiny devices. Pete Warden and Daniel Situnayake explain how you can train models small enough to fit into any environment. Ideal for software and hardware developers who want to build embedded systems using machine learning, this guide walks you through creating a series of TinyML projects, step-by-step. No machine learning or microcontroller experience is necessary. Build a speech recognizer, a camera that detects people, and a magic wand that responds to gestures Work with Arduino and ultra-low-power microcontrollers Learn the essentials of ML and how to train your own models Train models to understand audio, image, and accelerometer data Explore TensorFlow Lite for Microcontrollers, Google’s toolkit for TinyML Debug applications and provide safeguards for privacy and security Optimize latency, energy usage, and model and binary size
Publisher: O'Reilly Media
ISBN: 1492052019
Category : Computers
Languages : en
Pages : 504
Book Description
Deep learning networks are getting smaller. Much smaller. The Google Assistant team can detect words with a model just 14 kilobytes in size—small enough to run on a microcontroller. With this practical book you’ll enter the field of TinyML, where deep learning and embedded systems combine to make astounding things possible with tiny devices. Pete Warden and Daniel Situnayake explain how you can train models small enough to fit into any environment. Ideal for software and hardware developers who want to build embedded systems using machine learning, this guide walks you through creating a series of TinyML projects, step-by-step. No machine learning or microcontroller experience is necessary. Build a speech recognizer, a camera that detects people, and a magic wand that responds to gestures Work with Arduino and ultra-low-power microcontrollers Learn the essentials of ML and how to train your own models Train models to understand audio, image, and accelerometer data Explore TensorFlow Lite for Microcontrollers, Google’s toolkit for TinyML Debug applications and provide safeguards for privacy and security Optimize latency, energy usage, and model and binary size
Democratizing Innovation
Author: Eric Von Hippel
Publisher: MIT Press
ISBN: 0262250179
Category : Business & Economics
Languages : en
Pages : 224
Book Description
The process of user-centered innovation: how it can benefit both users and manufacturers and how its emergence will bring changes in business models and in public policy. Innovation is rapidly becoming democratized. Users, aided by improvements in computer and communications technology, increasingly can develop their own new products and services. These innovating users—both individuals and firms—often freely share their innovations with others, creating user-innovation communities and a rich intellectual commons. In Democratizing Innovation, Eric von Hippel looks closely at this emerging system of user-centered innovation. He explains why and when users find it profitable to develop new products and services for themselves, and why it often pays users to reveal their innovations freely for the use of all.The trend toward democratized innovation can be seen in software and information products—most notably in the free and open-source software movement—but also in physical products. Von Hippel's many examples of user innovation in action range from surgical equipment to surfboards to software security features. He shows that product and service development is concentrated among "lead users," who are ahead on marketplace trends and whose innovations are often commercially attractive. Von Hippel argues that manufacturers should redesign their innovation processes and that they should systematically seek out innovations developed by users. He points to businesses—the custom semiconductor industry is one example—that have learned to assist user-innovators by providing them with toolkits for developing new products. User innovation has a positive impact on social welfare, and von Hippel proposes that government policies, including R&D subsidies and tax credits, should be realigned to eliminate biases against it. The goal of a democratized user-centered innovation system, says von Hippel, is well worth striving for. An electronic version of this book is available under a Creative Commons license.
Publisher: MIT Press
ISBN: 0262250179
Category : Business & Economics
Languages : en
Pages : 224
Book Description
The process of user-centered innovation: how it can benefit both users and manufacturers and how its emergence will bring changes in business models and in public policy. Innovation is rapidly becoming democratized. Users, aided by improvements in computer and communications technology, increasingly can develop their own new products and services. These innovating users—both individuals and firms—often freely share their innovations with others, creating user-innovation communities and a rich intellectual commons. In Democratizing Innovation, Eric von Hippel looks closely at this emerging system of user-centered innovation. He explains why and when users find it profitable to develop new products and services for themselves, and why it often pays users to reveal their innovations freely for the use of all.The trend toward democratized innovation can be seen in software and information products—most notably in the free and open-source software movement—but also in physical products. Von Hippel's many examples of user innovation in action range from surgical equipment to surfboards to software security features. He shows that product and service development is concentrated among "lead users," who are ahead on marketplace trends and whose innovations are often commercially attractive. Von Hippel argues that manufacturers should redesign their innovation processes and that they should systematically seek out innovations developed by users. He points to businesses—the custom semiconductor industry is one example—that have learned to assist user-innovators by providing them with toolkits for developing new products. User innovation has a positive impact on social welfare, and von Hippel proposes that government policies, including R&D subsidies and tax credits, should be realigned to eliminate biases against it. The goal of a democratized user-centered innovation system, says von Hippel, is well worth striving for. An electronic version of this book is available under a Creative Commons license.
Synthesis and Optimization of Digital Circuits
Author: Giovanni De Micheli
Publisher: McGraw-Hill Science, Engineering & Mathematics
ISBN: 9780070163331
Category : Computer-aided design
Languages : en
Pages : 0
Book Description
The book provides a thorough explanation of synthesis and optimization algorithms accompanied by a sound mathematical formulation and a unified notation.
Publisher: McGraw-Hill Science, Engineering & Mathematics
ISBN: 9780070163331
Category : Computer-aided design
Languages : en
Pages : 0
Book Description
The book provides a thorough explanation of synthesis and optimization algorithms accompanied by a sound mathematical formulation and a unified notation.
Digital Microfluidic Biochips
Author: Krishnendu Chakrabarty
Publisher: CRC Press
ISBN: 1420008307
Category : Technology & Engineering
Languages : en
Pages : 228
Book Description
Digital Microfluidic Biochips focuses on the automated design and production of microfluidic-based biochips for large-scale bioassays and safety-critical applications. Bridging areas of electronic design automation with microfluidic biochip research, the authors present a system-level design automation framework that addresses key issues in the design, analysis, and testing of digital microfluidic biochips. The book describes a new generation of microfluidic biochips with more complex designs that offer dynamic reconfigurability, system scalability, system integration, and defect tolerance. Part I describes a unified design methodology that targets design optimization under resource constraints. Part II investigates cost-effective testing techniques for digital microfluidic biochips that include test resource optimization and fault detection while running normal bioassays. Part III focuses on different reconfiguration-based defect tolerance techniques designed to increase the yield and dependability of digital microfluidic biochips. Expanding upon results from ongoing research on CAD for biochips at Duke University, this book presents new design methodologies that address some of the limitations in current full-custom design techniques. Digital Microfluidic Biochips is an essential resource for achieving the integration of microfluidic components in the next generation of system-on-chip and system-in-package designs.
Publisher: CRC Press
ISBN: 1420008307
Category : Technology & Engineering
Languages : en
Pages : 228
Book Description
Digital Microfluidic Biochips focuses on the automated design and production of microfluidic-based biochips for large-scale bioassays and safety-critical applications. Bridging areas of electronic design automation with microfluidic biochip research, the authors present a system-level design automation framework that addresses key issues in the design, analysis, and testing of digital microfluidic biochips. The book describes a new generation of microfluidic biochips with more complex designs that offer dynamic reconfigurability, system scalability, system integration, and defect tolerance. Part I describes a unified design methodology that targets design optimization under resource constraints. Part II investigates cost-effective testing techniques for digital microfluidic biochips that include test resource optimization and fault detection while running normal bioassays. Part III focuses on different reconfiguration-based defect tolerance techniques designed to increase the yield and dependability of digital microfluidic biochips. Expanding upon results from ongoing research on CAD for biochips at Duke University, this book presents new design methodologies that address some of the limitations in current full-custom design techniques. Digital Microfluidic Biochips is an essential resource for achieving the integration of microfluidic components in the next generation of system-on-chip and system-in-package designs.
Machine Learning in VLSI Computer-Aided Design
Author: Ibrahim (Abe) M. Elfadel
Publisher: Springer
ISBN: 3030046664
Category : Technology & Engineering
Languages : en
Pages : 697
Book Description
This book provides readers with an up-to-date account of the use of machine learning frameworks, methodologies, algorithms and techniques in the context of computer-aided design (CAD) for very-large-scale integrated circuits (VLSI). Coverage includes the various machine learning methods used in lithography, physical design, yield prediction, post-silicon performance analysis, reliability and failure analysis, power and thermal analysis, analog design, logic synthesis, verification, and neuromorphic design. Provides up-to-date information on machine learning in VLSI CAD for device modeling, layout verifications, yield prediction, post-silicon validation, and reliability; Discusses the use of machine learning techniques in the context of analog and digital synthesis; Demonstrates how to formulate VLSI CAD objectives as machine learning problems and provides a comprehensive treatment of their efficient solutions; Discusses the tradeoff between the cost of collecting data and prediction accuracy and provides a methodology for using prior data to reduce cost of data collection in the design, testing and validation of both analog and digital VLSI designs. From the Foreword As the semiconductor industry embraces the rising swell of cognitive systems and edge intelligence, this book could serve as a harbinger and example of the osmosis that will exist between our cognitive structures and methods, on the one hand, and the hardware architectures and technologies that will support them, on the other....As we transition from the computing era to the cognitive one, it behooves us to remember the success story of VLSI CAD and to earnestly seek the help of the invisible hand so that our future cognitive systems are used to design more powerful cognitive systems. This book is very much aligned with this on-going transition from computing to cognition, and it is with deep pleasure that I recommend it to all those who are actively engaged in this exciting transformation. Dr. Ruchir Puri, IBM Fellow, IBM Watson CTO & Chief Architect, IBM T. J. Watson Research Center
Publisher: Springer
ISBN: 3030046664
Category : Technology & Engineering
Languages : en
Pages : 697
Book Description
This book provides readers with an up-to-date account of the use of machine learning frameworks, methodologies, algorithms and techniques in the context of computer-aided design (CAD) for very-large-scale integrated circuits (VLSI). Coverage includes the various machine learning methods used in lithography, physical design, yield prediction, post-silicon performance analysis, reliability and failure analysis, power and thermal analysis, analog design, logic synthesis, verification, and neuromorphic design. Provides up-to-date information on machine learning in VLSI CAD for device modeling, layout verifications, yield prediction, post-silicon validation, and reliability; Discusses the use of machine learning techniques in the context of analog and digital synthesis; Demonstrates how to formulate VLSI CAD objectives as machine learning problems and provides a comprehensive treatment of their efficient solutions; Discusses the tradeoff between the cost of collecting data and prediction accuracy and provides a methodology for using prior data to reduce cost of data collection in the design, testing and validation of both analog and digital VLSI designs. From the Foreword As the semiconductor industry embraces the rising swell of cognitive systems and edge intelligence, this book could serve as a harbinger and example of the osmosis that will exist between our cognitive structures and methods, on the one hand, and the hardware architectures and technologies that will support them, on the other....As we transition from the computing era to the cognitive one, it behooves us to remember the success story of VLSI CAD and to earnestly seek the help of the invisible hand so that our future cognitive systems are used to design more powerful cognitive systems. This book is very much aligned with this on-going transition from computing to cognition, and it is with deep pleasure that I recommend it to all those who are actively engaged in this exciting transformation. Dr. Ruchir Puri, IBM Fellow, IBM Watson CTO & Chief Architect, IBM T. J. Watson Research Center