03191nam a22004455i 4500001001800000003000900018005001700027007001500044008004100059020001800100024003500118050001500153072001600168072002300184072001500207245017200222250001800394264007500412300006700487336002600554337002600580338003600606347002400642505046800666520105401134650003102188650004402219650002902263650002202292650002802314650002902342700007902371700008102450710003402531773002602565776003602591776003602627776003602663856004602699978-3-031-19568-6DE-He21320251030110819.0cr nn 008mamaa230930s2024    sz |    s    |||| 0|eng d  a97830311956867 a10.1007/978-3-031-19568-62doi 4aTK7895.E42 7aUKM2bicssc 7aCOM0920002bisacsh 7aUKM2thema10aEmbedded Machine Learning for Cyber-Physical, IoT, and Edge Computingh[electronic resource] :bHardware Architectures /cedited by Sudeep Pasricha, Muhammad Shafique.  a1st ed. 2024. 1aCham :bSpringer International Publishing :bImprint: Springer,c2024.  aXIV, 412 p. 456 illus., 165 illus. in color.bonline resource.  atextbtxt2rdacontent  acomputerbc2rdamedia  aonline resourcebcr2rdacarrier  atext filebPDF2rda0 aIntroduction -- Efficient Hardware Acceleration for Embedded Machine Learning -- Memory Design and Optimization for Embedded Machine Learning -- Efficient Software Design of Embedded Machine Learning -- Hardware-Software Co-Design for Embedded Machine Learning -- Emerging Technologies for Embedded Machine Learning -- Mobile, IoT, and Edge Application Use-Cases for Embedded Machine Learning -- Cyber-Physical Application Use-Cases for Embedded Machine Learning.  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. 0aEmbedded computer systems. 0aCooperating objects (Computer systems). 0aArtificial intelligence.14aEmbedded Systems.24aCyber-Physical Systems.24aArtificial Intelligence.1 aPasricha, Sudeep.eeditor.4edt4http://id.loc.gov/vocabulary/relators/edt1 aShafique, Muhammad.eeditor.4edt4http://id.loc.gov/vocabulary/relators/edt2 aSpringerLink (Online service)0 tSpringer Nature eBook08iPrinted edition:z978303119567908iPrinted edition:z978303119569308iPrinted edition:z978303119570940uhttps://doi.org/10.1007/978-3-031-19568-6