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008			230425s2021 sz | s |||| 0|eng d
020			_a9783031017698
024	7		_a10.1007/978-3-031-01769-8 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQ325.5 _b2021 EB
100	1		_aRodriguez-Perez, Andres F. _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9688243 _d1980- _q(Andres Felipe),
245	1	0	_aDeep Learning Systems : _bAlgorithms, Compilers, and Processors for Large-Scale Production _cby Andres Rodriguez
250			_a1st edition 2021
264		1	_aCham _bSpringer International Publishing _c2021
300			_a1 recurso en línea (XX, 245 páginas)
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_aarchivo de texto _bPDF
490	0		_aSynthesis Lectures on Computer Architecture _x1935-3243
505	0		_aPreface -- Acknowledgments -- Introduction -- Building Blocks -- Models and Applications -- Training a Model -- Distributed Training -- Reducing the Model Size -- Hardware -- Compiler Optimizations -- Frameworks and Compilers -- Opportunities and Challenges -- Bibliography -- Author's Biography.
520			_aThis book describes deep learning systems: the algorithms, compilers, and processor components to efficiently train and deploy deep learning models for commercial applications. The exponential growth in computational power is slowing at a time when the amount of compute consumed by state-of-the-art deep learning (DL) workloads is rapidly growing. Model size, serving latency, and power constraints are a significant challenge in the deployment of DL models for many applications. Therefore, it is imperative to codesign algorithms, compilers, and hardware to accelerate advances in this field with holistic system-level and algorithm solutions that improve performance, power, and efficiency. Advancing DL systems generally involves three types of engineers: (1) data scientists that utilize and develop DL algorithms in partnership with domain experts, such as medical, economic, or climate scientists; (2) hardware designers that develop specialized hardware to accelerate the components in the DL models; and (3) performance and compiler engineers that optimize software to run more efficiently on a given hardware. Hardware engineers should be aware of the characteristics and components of production and academic models likely to be adopted by industry to guide design decisions impacting future hardware. Data scientists should be aware of deployment platform constraints when designing models. Performance engineers should support optimizations across diverse models, libraries, and hardware targets. The purpose of this book is to provide a solid understanding of (1) the design, training, and applications of DL algorithms in industry; (2) the compiler techniques to map deep learning code to hardware targets; and (3) the critical hardware features that accelerate DL systems. This book aims to facilitate co-innovation for the advancement of DL systems. It is written for engineers working in one or more of these areas who seek to understand the entire system stack in order to better collaborate with engineers working in other parts of the system stack. The book details advancements and adoption of DL models in industry, explains the training and deployment process, describes the essential hardware architectural features needed for today's and future models, and details advances in DL compilers to efficiently execute algorithms across various hardware targets. Unique in this book is the holistic exposition of the entire DL system stack, the emphasis on commercial applications, and the practical techniques to design models and accelerate their performance. The author is fortunate to work with hardware, software, data scientist, and research teams across many high-technology companies with hyperscale data centers. These companies employ many of the examples and methods provided throughout the book.
988			_aSynthesis Collection of Technology_2021
650		7	_2embne _9166090 _aAprendizaje automático
650		7	_2embne _aInteligencia artificial _9413115
650		7	_2embne _9151819 _aAlgoritmos computacionales
776	0	8	_iPrinted edition: _z9783031000669
776	0	8	_iPrinted edition: _z9783031006418
776	0	8	_iPrinted edition: _z9783031028977
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-031-01769-8 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b04/2023 _dz _eIG _zSI