This article has been reviewed according to Science X's editorial process and policies. Editors have highlighted the following attributes while ensuring the content's credibility: Engineers at the University of Wisconsin–Madison have designed a new type of quantum nanostructure that could enable optical neural networks. This emerging technology has the potential to make artificial intelligence systems, like large language models and image generation, faster and significantly more energy efficient.
The research, led by electrical and computer engineering Ph.D. students Qingyi Zhou and Jungmin Kim, computer science Ph.D. student Yutian Tao, and Zongfu Yu, a professor of electrical and computer engineering, was published in the journal Nature Communications on Aug. 27. Many of the most popular AI systems are based on deep neural networks, multilayer systems that mimic the interconnectedness of the human brain. As those systems scale in size and complexity, their energy consumption also increases.
That's one factor in recent concerns about AI energy use and data center construction. This hand-wringing over AI energy consumption is nothing new; in fact, a decade ago, AI researchers were aware that the energy cost of scaling neural networks was not sustainable. That's why they proposed something new: optical neural networks.
Instead of relying on traditional computer chips, optical systems use extremely tiny, fast lasers and photodetectors to process information. Theoretically, these systems are orders of magnitude faster and more efficient than the electronics-based GPUs that underpin current AI systems. In other words, they could do more work and use less energy.
But optical systems lack the one key attribute that makes a neural network more than just a fancy calculator. It's a quality called nonlinearity, which allows AI systems to transcend traditional computing. Optical systems are great at linear functions, where the output scales proportionally with the input.
But because photons—light particles that carry energy and information in optical systems—don't like to interact with one another, there are few optical materials that can produce nonlinear functions. Such nonlinear operations allow a network to learn complex patterns rather than just rescaling its inputs. While a few nonlinear optical materials do exist, the amount of laser energy needed to activate their nonlinear functions is so great that it negates the energy savings of the system.
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