000			03683nam a22004695i 4500
999			_c387229 _d387229
001			387229
003			ES-MaUEC
005			20230215192229.0
006			a||||fo|||| 00| 0
007			cr nn 008mamaa
008			220601s2020 sz | s |||| 0|eng d
020			_a9783031017667
024	7		_a10.1007/978-3-031-01766-7 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQA76.87 _b2020 EB
100	1		_aSze, Vivienne _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9686901
245	1	0	_aEfficient Processing of Deep Neural Networks _cby Vivienne Sze, Yu-Hsin Chen, Tien-Ju Yang, Joel S. Emer
250			_a1st edition 2020
264		1	_aCham _bSpringer International Publishing _c2020
300			_a1 recurso en línea (XXI, 254 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 -- Overview of Deep Neural Networks -- Key Metrics and Design Objectives -- Kernel Computation -- Designing DNN Accelerators -- Operation Mapping on Specialized Hardware -- Reducing Precision -- Exploiting Sparsity -- Designing Efficient DNN Models -- Advanced Technologies -- Conclusion -- Bibliography -- Authors' Biographies.
520			_aThis book provides a structured treatment of the key principles and techniques for enabling efficient processing of deep neural networks (DNNs). DNNs are currently widely used for many artificial intelligence (AI) applications, including computer vision, speech recognition, and robotics. While DNNs deliver state-of-the-art accuracy on many AI tasks, it comes at the cost of high computational complexity. Therefore, techniques that enable efficient processing of deep neural networks to improve key metrics-such as energy-efficiency, throughput, and latency-without sacrificing accuracy or increasing hardware costs are critical to enabling the wide deployment of DNNs in AI systems. The book includes background on DNN processing; a description and taxonomy of hardware architectural approaches for designing DNN accelerators; key metrics for evaluating and comparing different designs; features of DNN processing that are amenable to hardware/algorithm co-design to improve energy efficiency and throughput; and opportunities for applying new technologies. Readers will find a structured introduction to the field as well as formalization and organization of key concepts from contemporary work that provide insights that may spark new ideas.
988			_aSynthesis Collection of Technology_2020
650		7	_2embne _9678664 _aRedes neuronales artificiales _xAplicaciones industriales
650		7	_2embne _9495511 _aDatos masivos
650		7	_2embne _9166090 _aAprendizaje automático
700	1		_aChen, Yu-Hsin _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9686902 _c(Computer scientist)
700	1		_aYang, Tien-Ju _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9686903
700	1		_aEmer, Joel S., _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9686904 _d1954-
776	0	8	_iPrinted edition: _z9783031000638
776	0	8	_iPrinted edition: _z9783031006388
776	0	8	_iPrinted edition: _z9783031028946
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-031-01766-7 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b02/2023 _dz _esc _zSI