LEADER 02338nam 2200409 450 001 9910683386503321 005 20230702134818.0 010 $a3-0365-6689-9 024 7 $a10.3390/books978-3-0365-6689-4 035 $a(CKB)5700000000354365 035 $a(NjHacI)995700000000354365 035 $a(EXLCZ)995700000000354365 100 $a20230702d2023 uy 0 101 0 $aeng 135 $aur||||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aMemristive Devices and Systems $eModelling, Properties & Applications /$fChun Sing Lai, Zhekang Dong, Donglian Qi, editors 210 1$aBasel :$cMDPI - Multidisciplinary Digital Publishing Institute,$d2023. 215 $a1 online resource (218 pages) 311 $a3-0365-6688-0 320 $aIncludes bibliographical references. 330 $aThis reprint presents the Special Issue on "Memristive Devices and Systems: Modeling, Properties, and Applications". The Special Issue provides a comprehensive overview of key computational primitives enabled by these memory devices, as well as their applications, spanning edge computing, signal processing, optimization, machine learning, deep learning, stochastic computing, and so on. The memristor is considered to be a promising candidate for next-generation computing systems due to its nonvolatility, high density, low power, nanoscale geometry, nonlinearity, binary/multiple memory capacity, and negative differential resistance. Novel computing architectures/systems based on memristors have shown great potential to replace the traditional von Neumann computing architecture, which faces data movement challenges. With the development of material science, novel preparation and modeling methods for different memristive devices have been put forward recently, which opens up a new path for realizing different computing systems/architectures with practical memristor properties. 517 $aMemristive Devices and Systems 606 $aElectronics 615 0$aElectronics. 676 $a621.381 702 $aLai$b Chun Sing 702 $aDong$b Zhekang 702 $aQi$b Donglian 801 0$bNjHacI 801 1$bNjHacl 906 $aBOOK 912 $a9910683386503321 996 $aMemristive Devices and Systems$93394721 997 $aUNINA