Highly Efficient Synapse-Device-Based Neuromorphic Systems
A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Semiconductor Devices".
Deadline for manuscript submissions: closed (28 February 2021) | Viewed by 2955
Special Issue Editor
Special Issue Information
Recently, artificial intelligence (AI) has been significantly improved through brain-inspired neural networks and deep learning. In particular, in various tasks such as large-scale visual and auditory recognition/classification, the accuracy of the AI has surpassed human-level accuracy. However, to apply the AI on various user-based applications, hardware resources are still limited despite the various available approaches, such as hardware accelerators and CMOS ASICs. Therefore, as an alternative hardware platform, a synapse-device-based neuromorphic system including parallel matrix–vector multiplication has been proposed. Based on multilevel states and parallel connection of the synapse devices, weight storage and fast parallel matrix–vector multiplication can be realized. Therefore, various requirements, such as multilevel state and low power consumption of the synapse device, operation interference in array scale, circuital demonstration of learning rule/algorithm, etc. need to be satisfied. This Special Issue of Electronics aims to call for recent research on the synapse-device-based neuromorphic system from material to system scale.
Topic of Interests
Prospective authors are invited to submit original works and extended works based on the topics from a wide range of synapse-device-based neuromorphic systems. The synapse devices include (but are not limited to) the following devices: RRAM, CBRAM, FTJ (or other ferroelectric devices), STT-MRAM (or other spintronic devices), PCM, Flash (or other floating-gate/charge trap devices), etc. From material to circuitry, the following topics are solicited:
- Materials/devices for synapses and neurons;
- Brain-inspired neural networks with synapse/neuron devices;
- Selector materials/devices for crossbar array for neuromorphic systems;
- Array scale demonstration for neuromorphic systems;
- Architectural design/circuitry for neuromorphic systems;
- Learning algorithms and architecture/circuitry for neuromorphic systems.
Prof. Dr. Daeseok Lee
Guest Editor
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