AI Article Synopsis
- Recent advancements in generative AI have significantly impacted various sectors, but they consume a lot of energy and computational resources.
- Neuromorphic computing aims to develop efficient hardware that mimics biological neural networks, potentially enhancing next-generation computing.
- Memristors are promising components for neuromorphic systems, as they can replicate neural functions, and innovations in this area could help tackle complex computational problems.
Article Abstract
Recent generative artificial intelligence (AI) has exerted a profound and far-reaching global impact across diverse fields and society. However, it comes at the cost of substantial energy and computational resource consumption. Neuromorphic computing endeavors to create highly efficient computing hardware that emulates biological neural networks and even mimics some human brain functions, and it is expected to play an essential role in the next-generation computing hardware. Memristors open up novel opportunities for neuromorphic computing due to their feasible ability to mimic neural functions. Innovation in memristors may lead to novel algorithms and contribute to conventionally challenging tasks like nondeterministic polynomial time (NP)-hard problem. To this end, we present a themed collection in and , in which we publish the latest developments in memristive materials, device fabrication, characterization, and circuit design for neuromorphic systems.
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| http://dx.doi.org/10.1039/d4mh90052a | DOI Listing |
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