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dc.contributor.editorCho, Seongjae
dc.description.abstractThis 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.
dc.subject.classificationbic Book Industry Communication::T Technology, engineering, agriculture::TB Technology: general issues
dc.subject.classificationbic Book Industry Communication::K Economics, finance, business & management::KN Industry & industrial studies::KNB Energy industries & utilities
dc.subject.otherleaky integrate-and-fire neuron
dc.subject.othervanadium dioxide
dc.subject.otherneural network
dc.subject.otherpattern recognition
dc.subject.othera-IGZO memristor
dc.subject.otherSchottky barrier tunneling
dc.subject.othernon filamentary resistive switching
dc.subject.othergradual and abrupt modulation
dc.subject.otherbimodal distribution of effective Schottky barrier height
dc.subject.otherionized oxygen vacancy
dc.subject.otherenergy consumption
dc.subject.otherhardware-based neuromorphic system
dc.subject.othersynaptic device
dc.subject.otherSi processing compatibility
dc.subject.otherTCAD device simulation
dc.subject.otherbenchmarking neuromorphic HW
dc.subject.otherneuromorphic platform
dc.subject.otherMPI for neuromorphic HW
dc.subject.otherDNA matching algorithm
dc.subject.otherflexible electronics
dc.subject.otherneuromorphic engineering
dc.subject.otherorganic field-effect transistors
dc.subject.othersynaptic devices
dc.subject.othershort-term plasticity
dc.subject.otherneuromorphic system
dc.subject.otheron-chip learning
dc.subject.otheroverlapping pattern issue
dc.subject.otherspiking neural network
dc.subject.other3-D neuromorphic system
dc.subject.other3-D stacked synapse array
dc.subject.othercharge-trap flash synapse
dc.titleSemiconductor Memory Devices for Hardware-Driven Neuromorphic Systems
oapen.pages81, Switzerland

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