According to researchers from the University of Tokyo, an optical computer that is much more powerful than the existing ones may appear in only ten years.
A new method of optical computing called diffraction casting promises faster and more energy-efficient computers that are better suited for energy-intensive technologies like artificial intelligence. The approach proposed by a research team at the University of Tokyo uses light instead of electricity. Unlike traditional electronic devices, it works more efficiently, does not generate heat, and is not limited in size.
Diffraction casting uses layers of light to perform computations. According to the researchers, the method is particularly suitable for image processing and machine learning tasks. The method is based on an optical computing technique from the 1980s called shadow casting. However, diffraction casting improves on it by using the properties of light waves rather than bulky geometric shapes, creating more compact and flexible computing elements.
The system is still in its early stages, but the team from the University of Tokyo believes it could be commercially available within ten years and could pave the way for next-generation computing. The lead author of the study, Ryuosuke Mashiko, says that diffraction casting will not replace existing computing systems, but it can complement them in specialized tasks and potentially revolutionize this field. This method can also help future technologies such as quantum computing.
«I foresee that the technology will take about ten years to become commercially available, as there is still a lot of work to be done on the physical implementation, which, although based on real-world work, has yet to be created. For now, we can demonstrate the usefulness of diffraction tide for performing 16 basic logic operations that are the basis for processing large amounts of information,» Mashiko said.
The diffraction casting method is described in an article published in a peer-reviewed journal Advanced Photonics.
Source: Cybernews
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