MIT Researchers Develop Photonic Processor for Ultrafast, Energy-Efficient AI Computing

The breakthrough chip performs all key deep neural network computations optically, achieving nanosecond speeds and high accuracy.

The photonic processor processes machine-learning computations using light, offering significant speed and energy efficiency advantages over traditional electronic hardware.
Researchers designed a fully integrated optical neural network capable of performing both linear and nonlinear computations on the chip itself.
The chip achieved over 92% inference accuracy and completed key computations in less than half a nanosecond, comparable to conventional hardware performance.
Fabricated using commercial CMOS foundry processes, the chip has potential for scalable manufacturing and integration with existing electronic systems.
Future work aims to scale the device, integrate it with real-world applications such as telecommunications and navigation, and explore algorithms leveraging optical processing benefits.