| 000 | 02265nam a2200313 i 4500 | ||
|---|---|---|---|
| 999 |
_c401691 _d401691 |
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| 001 | 401691 | ||
| 003 | ES-MaUEC | ||
| 005 | 20240521115450.0 | ||
| 006 | a||||fo|||| 00| 0 | ||
| 007 | cr nn 008mamaa | ||
| 008 | 231009s2024 sz | fo |||| 0|eng d | ||
| 020 | _a9783031399329 | ||
| 024 | 7 |
_a10.1007/978-3-031-39932-9 _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 : _bSoftware Optimizations and Hardware/Software Codesign _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 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/10.1007/978-3-031-39932-9 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid) |
| 942 |
_2lcc _cLE |
||
| 998 |
_b05/2024 _dz _eb _zSI |
||