Semiconductor Memory Devices for Hardware-Driven Neuromorphic Systems

This 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 adva...

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Other Authors: Cho, Seongjae (Editor)
Format: Book Chapter
Published: Basel, Switzerland MDPI - Multidisciplinary Digital Publishing Institute 2021
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245 1 0 |a Semiconductor Memory Devices for Hardware-Driven Neuromorphic Systems 
260 |a Basel, Switzerland  |b MDPI - Multidisciplinary Digital Publishing Institute  |c 2021 
300 |a 1 electronic resource (81 p.) 
506 0 |a Open Access  |2 star  |f Unrestricted online access 
520 |a This 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. 
540 |a Creative Commons  |f https://creativecommons.org/licenses/by/4.0/  |2 cc  |4 https://creativecommons.org/licenses/by/4.0/ 
546 |a English 
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650 7 |a Energy industries & utilities  |2 bicssc 
653 |a leaky integrate-and-fire neuron 
653 |a vanadium dioxide 
653 |a neural network 
653 |a pattern recognition 
653 |a a-IGZO memristor 
653 |a Schottky barrier tunneling 
653 |a non filamentary resistive switching 
653 |a gradual and abrupt modulation 
653 |a bimodal distribution of effective Schottky barrier height 
653 |a ionized oxygen vacancy 
653 |a energy consumption 
653 |a hardware-based neuromorphic system 
653 |a synaptic device 
653 |a Si processing compatibility 
653 |a TCAD device simulation 
653 |a benchmarking neuromorphic HW 
653 |a neuromorphic platform 
653 |a spiNNaker 
653 |a spinMPI 
653 |a MPI for neuromorphic HW 
653 |a Boyer-Moore 
653 |a DNA matching algorithm 
653 |a flexible electronics 
653 |a neuromorphic engineering 
653 |a organic field-effect transistors 
653 |a synaptic devices 
653 |a short-term plasticity 
653 |a neuromorphic system 
653 |a on-chip learning 
653 |a overlapping pattern issue 
653 |a spiking neural network 
653 |a 3-D neuromorphic system 
653 |a 3-D stacked synapse array 
653 |a charge-trap flash synapse 
856 4 0 |a www.oapen.org  |u https://mdpi.com/books/pdfview/book/4351  |7 0  |z Get Fullteks 
856 4 0 |a www.oapen.org  |u https://directory.doabooks.org/handle/20.500.12854/76881  |7 0  |z DOAB: description of the publication