000			03526nam a22005295i 4500
001			978-3-031-19568-6
003			DE-He213
005			20251030110819.0
007			cr nn 008mamaa
008			230930s2024 sz | s |||| 0|eng d
020			_a9783031195686 _9978-3-031-19568-6
024	7		_a10.1007/978-3-031-19568-6 _2doi
050		4	_aTK7895.E42
072		7	_aUKM _2bicssc
072		7	_aCOM092000 _2bisacsh
072		7	_aUKM _2thema
245	1	0	_aEmbedded Machine Learning for Cyber-Physical, IoT, and Edge Computing _h[electronic resource] : _bHardware Architectures / _cedited by Sudeep Pasricha, Muhammad Shafique.
250			_a1st ed. 2024.
264		1	_aCham : _bSpringer International Publishing : _bImprint: Springer, _c2024.
300			_aXIV, 412 p. 456 illus., 165 illus. in color. _bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
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.
650		0	_aEmbedded computer systems. _964625
650		0	_aCooperating objects (Computer systems). _964303
650		0	_aArtificial intelligence. _958441
650	1	4	_aEmbedded Systems.
650	2	4	_aCyber-Physical Systems. _965638
650	2	4	_aArtificial Intelligence. _958441
700	1		_aPasricha, Sudeep. _eeditor. _4edt _4http://id.loc.gov/vocabulary/relators/edt _966329
700	1		_aShafique, Muhammad. _eeditor. _4edt _4http://id.loc.gov/vocabulary/relators/edt _966330
710	2		_aSpringerLink (Online service)
773	0		_tSpringer Nature eBook
776	0	8	_iPrinted edition: _z9783031195679
776	0	8	_iPrinted edition: _z9783031195693
776	0	8	_iPrinted edition: _z9783031195709
856	4	0	_uhttps://doi.org/10.1007/978-3-031-19568-6
912			_aZDB-2-ENG
912			_aZDB-2-SXE
999			_c56349 _d56349