LEADER 04007nam  22005895  450 
001 9910337466403321
005 20200704205005.0
010   $a3-319-92792-2
024 7 $a10.1007/978-3-319-92792-3
035   $a(CKB)4100000005958269
035   $a(MiAaPQ)EBC5496018
035   $a(DE-He213)978-3-319-92792-3
035   $a(PPN)229919022
035   $a(EXLCZ)994100000005958269
100   $a20180821d2019      u|         0
101 0 $aeng
135   $aurcnu||||||||
181   $ctxt$2rdacontent
182   $cc$2rdamedia
183   $acr$2rdacarrier
200 10$aHardware Accelerators in Data Centers /$fedited by Christoforos Kachris, Babak Falsafi, Dimitrios Soudris
205   $a1st ed. 2019.
210  1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2019.
215   $a1 online resource (280 pages) $cillustrations
311   $a3-319-92791-4 
320   $aIncludes bibliographical references and index.
327   $aIntroduction -- Building the Infrastructure for Deploying FPGAs in the Cloud -- dReDBox: A Disaggregated Architectural Perspective for Data Centers -- The Green Computing Continuum: The OPERA Perspective -- SPynq: Acceleration of Machine Learning Applications over Spark on Pynq -- M2DC - A Novel Heterogeneous Hyperscale Microserver Platform -- Towards an Energy-aware Framework for Application Development and Execution in Heterogeneous Parallel Architectures -- Enabling Virtualized Programmable Logic Resources at the Edge and the Cloud -- Energy Efficient Servers and Cloud -- Towards Ubiquitous Low-power Image Processing Platforms -- Energy-efficient Heterogeneous COmputing at exaSCALE - ECOSCALE -- On Optimizing the Energy Consumption of Urban Data Centers.
330   $aThis book provides readers with an overview of the architectures, programming frameworks, and hardware accelerators for typical cloud computing applications in data centers. The authors present the most recent and promising solutions, using hardware accelerators to provide high throughput, reduced latency and higher energy efficiency compared to current servers based on commodity processors. Readers will benefit from state-of-the-art information regarding application requirements in contemporary data centers, computational complexity of typical tasks in cloud computing, and a programming framework for the efficient utilization of the hardware accelerators. Provides a single-source reference to the state of the art for hardware accelerators in data centers; Describes integrated frameworks for the seamless deployment of hardware accelerators; Includes several use-case scenarios of hardware accelerators for typical cloud computing applications, such as machine learning, graph computation, and databases.
606   $aElectronic circuits
606   $aMicroprocessors
606   $aSignal processing
606   $aImage processing
606   $aSpeech processing systems
606   $aCircuits and Systems$3https://scigraph.springernature.com/ontologies/product-market-codes/T24068
606   $aProcessor Architectures$3https://scigraph.springernature.com/ontologies/product-market-codes/I13014
606   $aSignal, Image and Speech Processing$3https://scigraph.springernature.com/ontologies/product-market-codes/T24051
615  0$aElectronic circuits.
615  0$aMicroprocessors.
615  0$aSignal processing.
615  0$aImage processing.
615  0$aSpeech processing systems.
615 14$aCircuits and Systems.
615 24$aProcessor Architectures.
615 24$aSignal, Image and Speech Processing.
676   $a004.22
702   $aKachris$b Christoforos$4edt$4http://id.loc.gov/vocabulary/relators/edt
702   $aFalsafi$b Babak$4edt$4http://id.loc.gov/vocabulary/relators/edt
702   $aSoudris$b Dimitrios$4edt$4http://id.loc.gov/vocabulary/relators/edt
906   $aBOOK
912   $a9910337466403321
996   $aHardware Accelerators in Data Centers$92188140
997   $aUNINA