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 ...

Our latest and most advanced technologies — from AI to Industrial IoT, advanced robotics, and self-driving cars — share serious problems: massive energy consumption, limited on-edge capabilities, ...

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 collection supports and amplifies research related to SDG 9 - Industry, innovation and infrastructure. The exploration of two-dimensional materials has garnered significant attention in recent ...

Neuromorphic computing -- a field that applies principles of neuroscience to computing systems to mimic the brain's function and structure -- needs to scale up if it is to effectively compete with ...

A computing approach that requires up to 8,000 times less energy than conventional methods is emerging as a potential answer to one of technology’s most significant challenges: the unsustainable ...

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 ...

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 ...

Replicating the brain's capabilities, an impossible task, may theoretically require thousands of H100, one of NVIDIA's most ...

Developed by the Indian Institute of Science, the neuromorphic computing platform is designed to work alongside existing AI hardware, rather than replace it. The Indian Institute of Science (IISc) has ...