An international team comprised of 23 researchers has published a review article on the future of neuromorphic computing that examines the state of neuromorphic technology and presents a strategy for ...
Artificial intelligence (AI) has become a pivotal field in scientific research, yet its progress has been limited by reliance on massive datasets, high ...
Dhireesha Kudithipudi (second from right), founding director of MATRIX at UTSA, chats with students during the NSF AI Spring School at UTSA's San Pedro I building. The research is part of a broader ...
This review first revisits the theoretical background and developmental history of neuromorphic computing. It then briefly introduces the working mechanisms of memristive devices and how they can ...
A breakthrough in brain-inspired computing could make today’s energy-hungry AI systems far more efficient. Researchers have engineered a new nanoelectronic device using a modified form of hafnium ...
Large scale datasets and information processing requirements, within complex environments, are continuously reaching unprecedented levels of sophistication, especially in the advent of artificial ...
Los Alamos National Laboratory Researchers Design New Artificial Synapses for Neuromorphic Computing
Tested against a dataset of handwritten images from the Modified National Standards and Technology database, the interface-type memristors realized a high image recognition accuracy of 94.72%. (Los ...
A recent study published in npj 2D Materials and Applications explores hexagonal boron nitride (h-BN) atomristors, highlighting their notable memory window, low leakage current, and minimal power ...
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