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fliptop [soylentnews.org] writes:

Photonic circuits are a very promising technology for neural networks because they make it possible to build energy-efficient computing units. For years, the Politecnico di Milano [polimi.it] has been working on developing programmable photonic processors integrated on silicon microchips only a few mm2 in size for use in the field of data transmission and processing, and now these devices are being used to build photonic neural networks [techxplore.com]:

"An artificial neuron, like a biological neuron, must perform very simple mathematical operations, such as addition and multiplication, but in a neural network consisting of many densely interconnected neurons, the energy cost of these operations grows exponentially and quickly becomes prohibitive. Our chip incorporates a photonic accelerator that allows calculations to be carried out very quickly and efficiently, using a programmable grid of silicon interferometers. The calculation time is equal to the transit time of light in a chip a few millimeters in size, so we are talking about less than a billionth of a second (0.1 nanoseconds)," says Francesco Morichetti [polimi.it], Head of the Photonic Devices Lab of the Politecnico di Milano.

"The advantages of photonic neural networks have long been known, but one of the missing pieces to fully exploit their potential was network training.. It is like having a powerful calculator, but not knowing how to use it. In this study, we succeeded in implementing training strategies for photonic neurons similar to those used for conventional neural networks. The photonic 'brain' learns quickly and accurately and can achieve precision comparable to that of a conventional neural network, but faster and with considerable energy savings. These are all building blocks for artificial intelligence and quantum applications," adds Andrea Melloni [polimi.it], Director of Polifab the Politecnico di Milano micro and nanotechnology center.

Originally spotted on The Eponymous Pickle [blogspot.com].

Journal Reference: Sunil Pai et al, Experimentally realized in situ backpropagation for deep learning in photonic neural networks, Science (2023). DOI: 10.1126/science.ade8450 [doi.org]

Related: New Chip Can Process and Classify Nearly Two Billion Images Per Second [soylentnews.org]

Original Submission