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Название: Approximate Computing
Автор: Weiqiang Liu, Fabrizio Lombardi
Издательство: Springer
Год: 2022
Страниц: 607
Язык: английский
Формат: pdf (true), epub
Размер: 20.5 MB
This book explores the technological developments at various levels of abstraction, of the new paradigm of approximate computing. The authors describe in a single-source the state-of-the-art, covering the entire spectrum of research activities in approximate computing, bridging device, circuit, architecture, and system levels. Content includes tutorials, reviews and surveys of current theoretical/experimental results, design methodologies and applications developed in approximate computing for a wide scope of readership and specialists. Approximate computing has been proposed as a novel paradigm for efficient and low-power design at nanoscales. Efficiency is related to the computation of approximate results with at least comparable performance and lower power consumption compared to the fully accurate counterpart. Therefore, approximate computing generates results that are good enough rather than always fully accurate. Although computational errors generally are not desirable, applications such as multimedia, signal processing, Machine Learning (ML), pattern recognition, and data mining are tolerant to the occurrence of some errors.
Автор: Weiqiang Liu, Fabrizio Lombardi
Издательство: Springer
Год: 2022
Страниц: 607
Язык: английский
Формат: pdf (true), epub
Размер: 20.5 MB
This book explores the technological developments at various levels of abstraction, of the new paradigm of approximate computing. The authors describe in a single-source the state-of-the-art, covering the entire spectrum of research activities in approximate computing, bridging device, circuit, architecture, and system levels. Content includes tutorials, reviews and surveys of current theoretical/experimental results, design methodologies and applications developed in approximate computing for a wide scope of readership and specialists. Approximate computing has been proposed as a novel paradigm for efficient and low-power design at nanoscales. Efficiency is related to the computation of approximate results with at least comparable performance and lower power consumption compared to the fully accurate counterpart. Therefore, approximate computing generates results that are good enough rather than always fully accurate. Although computational errors generally are not desirable, applications such as multimedia, signal processing, Machine Learning (ML), pattern recognition, and data mining are tolerant to the occurrence of some errors.