Researchers at MIT have developed a photonic processor that can perform deep neural network computations optically on a chip, promising faster and more energy-efficient machine learning for demanding applications like lidar, astronomy, and high-speed telecommunications. The device, fabricated using commercial foundry processes, overcomes previous limitations by integrating nonlinear optical function units (NOFUs) to implement nonlinear operations on the chip. Led by Saumil Bandyopadhyay, a visiting scientist at MIT's Research Laboratory of Electronics, the team demonstrated a fully integrated photonic processor that can complete key computations in under half a nanosecond with over 92 percent accuracy. This breakthrough could enable real-time learning and high-speed processing for applications where speed and energy efficiency are critical. The research, published in Nature Photonics, was funded by the US National Science Foundation, the US Air Force Office of Scientific Research, and NTT Research, and involved collaboration with companies like Nokia and Periplous.
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