Semiconductor Memory Devices for Hardware-Driven Neuromorphic Systems

• Ētahi atu kaituhi: Cho, Seongjae (Editor)
• Hōputu: Tāhiko Wāhanga pukapuka
• Reo: Ingarihi
• I whakaputaina: Basel, Switzerland MDPI - Multidisciplinary Digital Publishing Institute 2021
• Ngā marau: Technology: general issues; Energy industries & utilities; leaky integrate-and-fire neuron; vanadium dioxide; neural network; pattern recognition; a-IGZO memristor; Schottky barrier tunneling; non filamentary resistive switching; gradual and abrupt modulation; bimodal distribution of effective Schottky barrier height; ionized oxygen vacancy; energy consumption; hardware-based neuromorphic system; synaptic device; Si processing compatibility; TCAD device simulation; benchmarking neuromorphic HW; neuromorphic platform; spiNNaker; spinMPI; MPI for neuromorphic HW; Boyer-Moore; DNA matching algorithm; flexible electronics; neuromorphic engineering; organic field-effect transistors; synaptic devices; short-term plasticity; neuromorphic system; on-chip learning; overlapping pattern issue; spiking neural network; 3-D neuromorphic system; 3-D stacked synapse array; charge-trap flash synapse
• Urunga tuihono: DOAB: download the publication; DOAB: description of the publication

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...