Solid-State Devices and Their Applications in Neuromorphic Computing
A special issue of Micromachines (ISSN 2072-666X). This special issue belongs to the section "E：Engineering and Technology".
Deadline for manuscript submissions: closed (15 April 2022) | Viewed by 336
Special Issue Editors
Interests: hardware for AI; spintronics for logic/memories; in-memory computing; neuromorphic computing; CMOS circuits
Interests: data science and artificial intelligence; electronic materials and devices; integrated circuits and systems; neuromorphic computing
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Neuromorphic computing aims at developing a new class of computational fabrics that are similar in structure and information processing capability as the human brain. Such neuromorphic computing platforms differ from state-of-the-art computing systems that are based on the conventional von-Neumann architecture. Recent works have shown significant power and performance area gains using neuromorphic systems for various classes of classification as well as for cognitive and control applications. Specifically, neuromorphic computing aims to mimic the dynamics of biological neurons and synapses with spike-based communications through novel solid-state devices and associated circuits that have beenco-designed closely with spike-based algorithms. A wide class of devices, including spintronic, resistive ram, phase change, chalcogenides, ferroelectric, optical devices, etc., is being widely explored to mimic neuro-synaptic dynamics and to enable the efficient mapping of spiking algorithms on such devices. This novel class of devices has also ushered in the use of novel computing paradigms, such as those for processing-in-memory to mitigate the bottlenecks associated with traditional computing platforms. Accordingly, this Special Issue seeks to showcase research papers, communications, and review articles that focus on (1) novel solid-state devices mimicking neuro-synaptic dynamics, (2) emerging paradigms such as processing-in-memory for neuromorphic computing, and (3) novel applications and algortihms exploiting emerging neuromorphic devices .
Dr. Akhilesh Jaiswal
Dr. Abhronil Sengupta
Dr. Deliang Fan
Guest Editors
Manuscript Submission Information
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Keywords
- neuromorphic devices
- solid-state neurons
- solid-state synapses
- spiking neural networks
- event-driven computations
- spike-timing-dependent plasticity
- spike-based learning
- analog computing
- processing in memory
