Artificial intelligence is now part of our daily lives, with the subsequent pressing need for larger, more complex models. However, the demand for ever-increasing power and computing capacity is rising faster than the performance traditional computers can provide.
To overcome these limitations, research is moving towards innovative technologies such as physical neural networks, analog circuits that directly exploit the laws of physics (properties of light beams, quantum phenomena) to process information. Their potential is at the heart of the study published in the journal Nature. It is the outcome of collaboration between several international institutes, including the Politecnico di Milano, the École Polytechnique Fédérale in Lausanne, Stanford University, the University of Cambridge, and the Max Planck Institute.
The article entitled “Training of Physical Neural Networks” discusses the steps of research on training physical neural networks, carried out with the collaboration of Francesco Morichetti, professor at DEIB—Department of Electronics, Information and Bioengineering, and head of the university’s Photonic Devices Lab.
Politecnico di Milano contributed to this study by developing photonic chips for the creation of neural networks, exploiting integrated photonic technologies. Mathematical operations, such as sums and multiplications, can now be performed through light interference mechanisms on silicon microchips barely a few square millimeters in size.
“By eliminating the operations required for the digitization of information, our photonic chips allow calculations to be carried out with a significant reduction in both energy consumption and processing time,” says Morichetti. A step forward to make artificial intelligence (which relies on extremely energy-intensive data centers) more sustainable.
The study addresses the theme of training, precisely the phase in which the network learns to perform certain tasks. “With our research within the Department of Electronics, Information and Bioengineering, we have helped develop an ‘in-situ’ training technique for photonic neural networks, i.e. without going through digital models. The procedure is carried out entirely using light signals. Hence, network training will not only be faster, but also more robust and efficient,” adds Morichetti.
The use of photonic chips will allow the development of more sophisticated models for artificial intelligence, or devices capable of processing real-time data directly on site—such as autonomous cars or intelligent sensors integrated into portable devices—without requiring remote processing.
More information:
Ali Momeni et al, Training of physical neural networks, Nature (2025). DOI: 10.1038/s41586-025-09384-2
Polytechnic University of Milan
Citation:
Sustainable AI: Physical neural networks exploit light to train more efficiently (2025, September 9)
retrieved 9 September 2025
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