LEADER 03393nam 22006015 450 001 9910760273503321 005 20250807133322.0 010 $a9783031195686 010 $a303119568X 024 7 $a10.1007/978-3-031-19568-6 035 $a(MiAaPQ)EBC30765507 035 $a(Au-PeEL)EBL30765507 035 $a(DE-He213)978-3-031-19568-6 035 $a(PPN)272738883 035 $a(CKB)28443977500041 035 $a(OCoLC)1402024338 035 $a(EXLCZ)9928443977500041 100 $a20230930d2024 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aEmbedded Machine Learning for Cyber-Physical, IoT, and Edge Computing $eHardware Architectures /$fedited by Sudeep Pasricha, Muhammad Shafique 205 $a1st ed. 2024. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2024. 215 $a1 online resource (418 pages) 311 08$a9783031195679 311 08$a3031195671 327 $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. 330 $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. 606 $aEmbedded computer systems 606 $aCooperating objects (Computer systems) 606 $aArtificial intelligence 606 $aEmbedded Systems 606 $aCyber-Physical Systems 606 $aArtificial Intelligence 615 0$aEmbedded computer systems. 615 0$aCooperating objects (Computer systems) 615 0$aArtificial intelligence. 615 14$aEmbedded Systems. 615 24$aCyber-Physical Systems. 615 24$aArtificial Intelligence. 676 $a006.22 702 $aPasricha$b Sudeep 702 $aShafique$b Muhammad 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910760273503321 996 $aEmbedded machine learning for cyber-physical, IoT, and edge computing$94168851 997 $aUNINA