000			02714nam a2200325 i 4500
999			_c401659 _d401659
001			401659
003			ES-MaUEC
005			20240520160038.0
006			a||||fo|||| 00| 0
007			cr nn 008mamaa
008			230930s2024 sz | fo |||| 0|eng d
020			_a9783031195686
024	7		_a10.1007/978-3-031-19568-6 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aTK7885 _b2024 EB
245	0	0	_aEmbedded Machine Learning for Cyber-Physical, IoT, and Edge Computing : _bHardware Architectures _cedited by Sudeep Pasricha, Muhammad Shafique
250			_a1st ed. 2024
264		1	_aCham _bSpringer International Publishing _c2024
300			_a1 recurso en línea
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
505	0		_aIntroduction -- Efficient Hardware Acceleration for Embedded Machine Learning -- Memory Design and Optimization for Embedded Machine Learning -- Efficient Software Design of Embedded Machine Learning -- Hardware-Software Co-Design for Embedded Machine Learning -- Emerging Technologies for Embedded Machine Learning -- Mobile, IoT, and Edge Application Use-Cases for Embedded Machine Learning -- Cyber-Physical Application Use-Cases for Embedded Machine Learning.
520			_aThis book presents recent advances towards the goal of enabling efficient implementation of machine learning models on resource-constrained systems, covering different application domains. The focus is on presenting interesting and new use cases of applying machine learning to innovative application domains, exploring the efficient hardware design of efficient machine learning accelerators, memory optimization techniques, illustrating model compression and neural architecture search techniques for energy-efficient and fast execution on resource-constrained hardware platforms, and understanding hardware-software codesign techniques for achieving even greater energy, reliability, and performance benefits. Discusses efficient implementation of machine learning in embedded, CPS, IoT, and edge computing; Offers comprehensive coverage of hardware design, software design, and hardware/software co-design and co-optimization; Describes real applications todemonstrate how embedded, CPS, IoT, and edge applications benefit from machine learning.
988			_aSpringer_Engineering_2024
650		7	_2embne _9167668 _aIngeniería de ordenadores
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-031-19568-6 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b05/2024 _dz _eb _zSI