000 02558nam a22004095i 4500
006 a||||fo|||| 00| 0
007 cr nn nnnaamaa
710 2 _aSpringerLink (Online service)
_9106996
999 _c111481
_d111481
_x1
001 111481
003 ES-MaUEC
005 20230102113514.0
008 181207s2019 si a o |||| 0|eng d
020 _a9789811333231
024 7 _a10.1007/978-981-13-3323-1
_2doi
040 _bspa
_dES-MaUEC
_cES-MaUEC
050 4 _aQ325.5
_b2019 EB
100 1 _aHuang, Hantao
_eautor
_9671255
245 1 0 _aCompact and Fast Machine Learning Accelerator for IoT Devices
_cby Hantao Huang, Hao Yu.
264 1 _aSingapore
_bImprint: Springer
_c2019
300 _a1 recurso en línea (IX, 149 páginas)
336 _2rdacontent
_aTexto
_btxt
337 _2rdamedia
_aelectrónico
_bc
338 _2rdacarrier
_arecurso electrónico
_bcr
347 _atext file
_bPDF
_2rda
490 0 _aComputer Architecture and Design Methodologies
_x2367-3478
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
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)
942 _2lcc
_cLE
998 _aSI
_cm
_dz
_feng
_ggw
_h0
_b11/2019
_ek
_zSI