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_aSpringerLink (Online service) _9106996 |
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| 003 | ES-MaUEC | ||
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| 008 | 181207s2019 si a o |||| 0|eng d | ||
| 020 | _a9789811333231 | ||
| 024 | 7 |
_a10.1007/978-981-13-3323-1 _2doi |
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| 040 |
_bspa _dES-MaUEC _cES-MaUEC |
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| 050 | 4 |
_aQ325.5 _b2019 EB |
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| 100 | 1 |
_aHuang, Hantao _eautor _9671255 |
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| 245 | 1 | 0 |
_aCompact and Fast Machine Learning Accelerator for IoT Devices _cby Hantao Huang, Hao Yu. |
| 264 | 1 |
_aSingapore _bImprint: Springer _c2019 |
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| 300 | _a1 recurso en línea (IX, 149 páginas) | ||
| 336 |
_2rdacontent _aTexto _btxt |
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| 337 |
_2rdamedia _aelectrónico _bc |
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| 338 |
_2rdacarrier _arecurso electrónico _bcr |
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| 347 |
_atext file _bPDF _2rda |
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| 490 | 0 |
_aComputer Architecture and Design Methodologies _x2367-3478 |
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| 490 | 0 | _aIntelligent Technologies and Robotics (Springer-42732) | |
| 505 | 0 | _aComputing on Edge Devices in Internet-of-things (IoT) -- The Rise of Machine Learning in IoT system -- Least-squares-solver for Shadow Neural Network -- Tensor-solver for Deep Neural Network -- Distributed-solver for Networked Neural Network -- Conclusion. | |
| 520 | 3 | _aThis book presents the latest techniques for machine learning based data analytics on IoT edge devices. A comprehensive literature review on neural network compression and machine learning accelerator is presented from both algorithm level optimization and hardware architecture optimization. Coverage focuses on shallow and deep neural network with real applications on smart buildings. The authors also discuss hardware architecture design with coverage focusing on both CMOS based computing systems and the new emerging Resistive Random-Access Memory (RRAM) based systems. Detailed case studies such as indoor positioning, energy management and intrusion detection are also presented for smart buildings. | |
| 988 | _aPrimersemestre_2019_Robotics | ||
| 650 | 7 |
_2embne _aAprendizaje automático _9166090 |
|
| 700 | 1 |
_aYu, Hao. _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut |
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| 776 | 0 | 8 |
_iPrinted edition: _z9789811333224 |
| 776 | 0 | 8 |
_iPrinted edition: _z9789811333248 |
| 856 | 4 | 0 |
_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-981-13-3323-1 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid) |
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_2lcc _cLE |
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| 998 |
_aSI _cm _dz _feng _ggw _h0 _b11/2019 _ek _zSI |
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