000 02246nam a2200313 i 4500
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003 ES-MaUEC
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020 _a9783031406775
024 7 _a10.1007/978-3-031-40677-5
_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 :
_bUse Cases and Emerging Challenges
_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
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 to demonstrate 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/10.1007/978-3-031-40677-5
_zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942 _2lcc
_cLE
998 _b05/2024
_dz
_eb
_zSI