Neuromorphic Device, Circuits, and Systems
A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Circuit and Signal Processing".
Deadline for manuscript submissions: 15 April 2025 | Viewed by 3062
Special Issue Editors
Interests: VLSI circuit design; neuromorphic computing hardware; photodetector design; integrated smart sensors for biomedical applications
Interests: neuromorphic computing hardware design; nano-enabled hardware security; memristor device modeling and simulation; nanoelectronic circuit design for emerging computing architectures
Special Issue Information
Dear Colleagues,
Neuromorphic computing has become an attractive candidate for emerging computing platforms. Using principles from biology, neuromorphic computing creates engineered circuits and systems that function like living organisms. Despite their VLSI origins, neuromorphic circuits that use VLSI semiconductors are advancing increasingly and moving further away from the von Neumann generation. Based on their biological counterparts, neuromorphic computing systems have also become increasingly advanced as technology has advanced. Artificial intelligence and learning can be demonstrated along with the evolution of emerging devices, circuits, and systems that more closely resemble their biological prototypes. As neuromorphic devices, circuits, and systems continue to develop, they mimic the brain's computational primitives more closely in terms of efficiency, functionality, and plasticity.
The purpose of this Special Issue is to discuss the state of the art in terms of devices, circuits, architecture, analysis, and optimization for neuromorphic computing systems. It also discusses the design and development of neuromorphic computing devices and hardware and neuromorphic learning algorithms using emerging circuits and devices.
Dr. Mst Shamim Ara Shawkat
Dr. Garrett S. Rose
Guest Editors
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Keywords
- devices, circuits, architecture design, analysis, and optimization for neuromorphic computing systems
- design and development of neuromorphic computing devices and hardware
- neuromorphic learning algorithms using emerging devices and circuits
- design of novel artificial neural networks and systems
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