Researchers have developed a way to use sound waves in optical neural networks, enhancing their ability to process data with high speed and energy efficiency.
Optical neural networks may provide the high-speed and large-capacity solution necessary to tackle challenging computing tasks. However, tapping their full potential will require further advances. One challenge is the reconfigurability of optical neural networks.
A research team in the Stiller Research Group at the Max Planck Institute for the Science of Light, in collaboration with the Englund Research Group at the Massachusetts Institute of Technology, has now succeeded in laying the foundation for new reconfigurable neuromorphic building blocks by adding a new dimension to photonic machine learning: sound waves.
The researchers use light to create temporary acoustic waves in an optical fiber. The sound waves generated in this way can for instance enable a recurrent functionality in a telecom optical fiber, which is essential to interpreting contextual information such as language.
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