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Photonic hardware, which can perform machine-learning computations with light, offers a faster and more energy-efficient alternative.

However, there are some types of neural network computations that a photonic device can't perform, requiring the use of off-chip electronics or other techniques that hamper speed and efficiency.

Building on a decade of research, scientists from MIT and elsewhere have developed a new photonic chip that overcomes these roadblocks.

They demonstrated a fully integrated photonic processor that can perform all the key computations of a deep neural network optically on the chip.

In the long run, the photonic processor could lead to faster and more energy-efficient deep learning for computationally demanding applications like lidar, scientific research in astronomy and particle physics, or high-speed telecommunications.