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008			230425s2017 sz | s |||| 0|eng d
020			_a9783031017568
024	7		_a10.1007/978-3-031-01756-8 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQ325.5 _b2017 EB
100	1		_aReagen, Brandon _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9688236
245	1	0	_aDeep Learning for Computer Architects _cby Brandon Reagen, Robert Adolf, Paul Whatmough, Gu-Yeon Wei, David Brooks
250			_a1st edition 2017
264		1	_aCham _bSpringer International Publishing _c2017
300			_a1 recurso en línea (XIV, 109 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 -- Introduction -- Foundations of Deep Learning -- Methods and Models -- Neural Network Accelerator Optimization: A Case Study -- A Literature Survey and Review -- Conclusion -- Bibliography -- Authors' Biographies.
520			_aMachine learning, and specifically deep learning, has been hugely disruptive in many fields of computer science. The success of deep learning techniques in solving notoriously difficult classification and regression problems has resulted in their rapid adoption in solving real-world problems. The emergence of deep learning is widely attributed to a virtuous cycle whereby fundamental advancements in training deeper models were enabled by the availability of massive datasets and high-performance computer hardware. This text serves as a primer for computer architects in a new and rapidly evolving field. We review how machine learning has evolved since its inception in the 1960s and track the key developments leading up to the emergence of the powerful deep learning techniques that emerged in the last decade. Next we review representative workloads, including the most commonly used datasets and seminal networks across a variety of domains. In addition to discussing the workloads themselves, we also detail the most popular deep learning tools and show how aspiring practitioners can use the tools with the workloads to characterize and optimize DNNs. The remainder of the book is dedicated to the design and optimization of hardware and architectures for machine learning. As high-performance hardware was so instrumental in the success of machine learning becoming a practical solution, this chapter recounts a variety of optimizations proposed recently to further improve future designs. Finally, we present a review of recent research published in the area as well as a taxonomy to help readers understand how various contributions fall in context.
988			_aSynthesis Collection of Technology_2017
650		7	_2embne _9166090 _aAprendizaje automático
650		7	_2embne _9678664 _aRedes neuronales artificiales
700	1		_aAdolf, Robert _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9688237 _c(Computer scientist)
700	1		_aWhatmough, Paul _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9688238
700	1		_aWei, Gu-Yeon _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9688239
700	1		_aBrooks, David, _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9686870 _d1975-
776	0	8	_iPrinted edition: _z9783031000546
776	0	8	_iPrinted edition: _z9783031006289
776	0	8	_iPrinted edition: _z9783031028847
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-031-01756-8 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b04/2023 _dz _eIG _zSI