International team of researchers develops photonic processor with adaptive neural connectivity
The chip contains almost 8,400 functioning artificial neurons from waveguide-coupled phase-change material. The researchers trained this neural network to distinguish between German and English texts on the basis of vowel frequency. Modern computer models - for example for complex, potent AI applications - push traditional digital computer processes to their limits. New types of computing architecture, which emulate the working principles of biological neural networks, hold the promise of faster, more energy-efficient data processing. A team of researchers has now developed a so-called event-based architecture, using photonic processors with which data are transported and processed by means of light. In a similar way to the brain, this makes possible the continuous adaptation of the connections within the neural network. This changeable connections are the basis for learning processes.
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