This AI chip doesn’t use electricity to compute — it uses light.
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Engineers at the University of Pennsylvania have built a photonic neural network capable of classifying nearly 2 billion images per second, operating at speeds millions of times faster than today’s electronic computer vision systems.
In this video, we explore how photonic neural networks work, why traditional image recognition is so computationally expensive, and how light-based hardware could overcome fundamental limits of GPUs and silicon. We go over how convolution layers, weighted sums, and activation functions are implemented directly on a photonic chip — without memory, clock cycles, or digital logic.
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