Recent advances in materials, devices, and system architectures are driving a new generation of computing beyond traditional CMOS, with neuromorphic ...
Abstract: Reservoir computing (RC), a neuromorphic computing paradigm enabling universal approximation, faces challenges in structural complexity and energy efficiency, particularly in hardware ...
Indian American computer scientist Dhireesha Kudithipudi is leading a shift in the American technological landscape, moving artificial intelligence away from power-hungry data centers and toward a ...
When you swing a tennis racket or catch a set of keys, you aren’t thinking about wind resistance or gravity. Yet, to perform that motion, your brain is solving a massive physics problem in ...
Neuromorphic computers modeled after the human brain can now solve the complex equations behind physics simulations — something once thought possible only with energy-hungry supercomputers. The ...
Interdisciplinary research between neuromorphic and deep learning paradigms has developed rapidly in recent years. Key advances include silicon nano-devices and memristors for neuromorphic computing, ...
Abstract: Recently, consumer electronics have moved toward data-centric applications due to the development of artificial intelligence (AI) technologies. With the growing demand for large memory ...
The growth and impact of artificial intelligence are limited by the power and energy that it takes to train machine learning models. So how are researchers working to improve computing efficiency to ...
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