Light Speed Machine Learning With Photonics

Substituting a photonic tensor core for existing digital processors such as GPUs, engineers have introduced a new technique for performing high-level neural network computations. In the approach, light speed energy replaces electricity, processing optical data feeds at a performance rate two to three orders higher than with an electrical tensor processing unit (TPU) and supporting unsupervised learning and performance in AI machines.

Neural networks commonly perform and advance machine learning, meaning the discovery has the potential to develop artificial intelligence for a variety of applications. Neural networks in machine learning are trained to classify unseen data and make unsupervised decisions based on information. Once trained on that data, a neural network can formulate an inference to identify and classify objects and patterns giving data a unique signature.

In the new system, the light speed photonic TPU serves to improve both the speed and efficiency of existing deep learning paradigms by performing multiplications of matrices in parallel. It relies on an electro-optical interconnect, allowing an efficient reading and writing of the optical memory, and the TPU to interface with additional architectures.

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