LEADER 04156nam 2200769z- 450 001 9910557566603321 005 20220111 035 $a(CKB)5400000000043961 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/76881 035 $a(oapen)doab76881 035 $a(EXLCZ)995400000000043961 100 $a20202201d2021 |y 0 101 0 $aeng 135 $aurmn|---annan 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aSemiconductor Memory Devices for Hardware-Driven Neuromorphic Systems 210 $aBasel, Switzerland$cMDPI - Multidisciplinary Digital Publishing Institute$d2021 215 $a1 online resource (81 p.) 311 08$a3-0365-1734-0 311 08$a3-0365-1733-2 330 $aThis book aims to convey the most recent progress in hardware-driven neuromorphic systems based on semiconductor memory technologies. Machine learning systems and various types of artificial neural networks to realize the learning process have mainly focused on software technologies. Tremendous advances have been made, particularly in the area of data inference and recognition, in which humans have great superiority compared to conventional computers. In order to more effectively mimic our way of thinking in a further hardware sense, more synapse-like components in terms of integration density, completeness in realizing biological synaptic behaviors, and most importantly, energy-efficient operation capability, should be prepared. For higher resemblance with the biological nervous system, future developments ought to take power consumption into account and foster revolutions at the device level, which can be realized by memory technologies. This book consists of seven articles in which most recent research findings on neuromorphic systems are reported in the highlights of various memory devices and architectures. Synaptic devices and their behaviors, many-core neuromorphic platforms in close relation with memory, novel materials enabling the low-power synaptic operations based on memory devices are studied, along with evaluations and applications. Some of them can be practically realized due to high Si processing and structure compatibility with contemporary semiconductor memory technologies in production, which provides perspectives of neuromorphic chips for mass production. 606 $aEnergy industries & utilities$2bicssc 606 $aTechnology: general issues$2bicssc 610 $a3-D neuromorphic system 610 $a3-D stacked synapse array 610 $aa-IGZO memristor 610 $abenchmarking neuromorphic HW 610 $abimodal distribution of effective Schottky barrier height 610 $aBoyer-Moore 610 $acharge-trap flash synapse 610 $aDNA matching algorithm 610 $aenergy consumption 610 $aflexible electronics 610 $agradual and abrupt modulation 610 $ahardware-based neuromorphic system 610 $aionized oxygen vacancy 610 $aleaky integrate-and-fire neuron 610 $aMPI for neuromorphic HW 610 $aneural network 610 $aneuromorphic engineering 610 $aneuromorphic platform 610 $aneuromorphic system 610 $anon filamentary resistive switching 610 $aon-chip learning 610 $aorganic field-effect transistors 610 $aoverlapping pattern issue 610 $apattern recognition 610 $aSchottky barrier tunneling 610 $ashort-term plasticity 610 $aSi processing compatibility 610 $aspiking neural network 610 $aspinMPI 610 $aspiNNaker 610 $asynaptic device 610 $asynaptic devices 610 $aTCAD device simulation 610 $avanadium dioxide 615 7$aEnergy industries & utilities 615 7$aTechnology: general issues 700 $aCho$b Seongjae$4edt$01310439 702 $aCho$b Seongjae$4oth 906 $aBOOK 912 $a9910557566603321 996 $aSemiconductor Memory Devices for Hardware-Driven Neuromorphic Systems$93029819 997 $aUNINA