02855nam a22004695i 4500001001800000003000900018005001700027007001500044008004100059020003700100024003500137050001500172072001600187072002300203072001500226245018300241250001800424264006900442300006600511336002600577337002600603338003600629347002400665520105500689650003801744650002501782650005101807650002201858650003701880650003501917700008601952700008802038710003402126773002602160776003602186776003602222776003602258856004602294912001402340912001402354999001702368978-3-031-40677-5DE-He21320251030110821.0cr nn 008mamaa231006s2024    sz |    s    |||| 0|eng d  a97830314067759978-3-031-40677-57 a10.1007/978-3-031-40677-52doi 4aTK7895.E42 7aUKM2bicssc 7aCOM0920002bisacsh 7aUKM2thema10aEmbedded Machine Learning for Cyber-Physical, IoT, and Edge Computingh[electronic resource] :bUse Cases and Emerging Challenges /cedited by Sudeep Pasricha, Muhammad Shafique.  a1st ed. 2024. 1aCham :bSpringer Nature Switzerland :bImprint: Springer,c2024.  aXV, 571 p. 220 illus., 211 illus. in color.bonline resource.  atextbtxt2rdacontent  acomputerbc2rdamedia  aonline resourcebcr2rdacarrier  atext filebPDF2rda  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. 0aEmbedded computer systems.964625 0aElectronic circuits. 0aCooperating objects (Computer systems).96430314aEmbedded Systems.24aElectronic Circuits and Systems.24aCyber-Physical Systems.9656381 aPasricha, Sudeep.eeditor.4edt4http://id.loc.gov/vocabulary/relators/edt9663291 aShafique, Muhammad.eeditor.4edt4http://id.loc.gov/vocabulary/relators/edt9663302 aSpringerLink (Online service)0 tSpringer Nature eBook08iPrinted edition:z978303140676808iPrinted edition:z978303140678208iPrinted edition:z978303140679940uhttps://doi.org/10.1007/978-3-031-40677-5  aZDB-2-ENG  aZDB-2-SXE  c56376d56376