Semiconductor Memory Devices for Hardware-Driven Neuromorphic Systems

Dear Colleagues,

This Special Issue aims to convey the most recent progresses in hardware-driven neuromorphic systems. The machine learning system and various types of artificial neural networks to realize the learning process have been mainly focused on software technologies. Tremendous advances have been made particularly in the area of recognition, in which humans have great superiority compared to computers. For higher resemblance with the biological nervous system, the upcoming progresses are ought to take power consumption into account and foster revolutions in the electron devices and integrated circuits that make up the neuromorphic system.

For this Special Issue, we cordially invite contributions related to state-of-the-art technologies for neuromorphic systems more intimately oriented to hardware developments. Topics of interest include but are not limited to: mathematical and physical theories and modeling with practical implications in hardware representation, synaptic components including novel solid-state logic and memory devices, neuron circuits with power-efficient transistors, metallic and optical interconnects, novel software algorithms optimally prepared for specific types of synaptic devices, floor plans for synapse and neuron circuits in either two- or three-dimensional architectures, emulation of neural chips compatible with conventional computers, measurement techniques, and time- and cost-effective process integration for realizing these systems.

Prof. Dr. Seongjae Cho
Guest Editor

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