In-situ training of photonic/quantum analogue neural networks: faster, more robust/ efficient than digital

https://www.eurekalert.org/news-releases/1097680

"math operations performed through light interference mechanisms on mm-scale silicon microchips carried out faster/ using less energy, since no need to digitize information... step forward to make artificial intelligence (which relies on extremely energy-intensive data centers) more sustainable... addresses theme of training, phase in which network learns to perform certain tasks... also enables more sophisticated AI models integrating real-time data processing into portable devices... autonomous cars, intelligent sensors"

Related: Advanced design for high‑performance and AI chips

https://www.eurekalert.org/news-releases/1101475