Penn engineers have developed the first photonic chip that reshapes how light behaves to carry out the nonlinear mathematics at the heart of modern AI while reducing energy use.
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Researchers at Penn’s School of Engineering and Applied Science have developed the first programmable chip that can train nonlinear neural networks using light—a breakthrough that could dramatically speed up AI training, reduce energy use, and even pave the way for fully light-powered computers.
Tianwei Wu (left) and Liang Feng (right) demonstrating some of the apparatus used to develop the new, light-powered chip.
(Image: Sylvia Zhang)
While today’s AI chips are electronic and rely on electricity to perform calculations, the new chip is photonic, meaning it uses beams of light instead. Described in Nature Photonics, the chip reshapes how light behaves to carry out the nonlinear mathematics at the heart of modern AI.
“Nonlinear functions are critical for training deep neural networks,” says Liang Feng, professor in materials science and engineering (MSE) and in electrical and systems Engineering (ESE), and the paper’s senior author. “Our aim was to make this happen in photonics for the first time.”
Most AI systems today depend on neural networks, software designed to mimic biological neural tissue. Just as neurons connect to allow biological creatures to think, neural networks link together layers of simple units, or “nodes,” enabling AI systems to perform complex tasks.
In both artificial and biological systems, these nodes only “fire” once a threshold is reached—a nonlinear process that allows small changes in input to cause larger, more complex changes in output.
While many research teams, including teams at Penn Engineering, have developed light-powered chips capable of handling linear mathematical operations, none has solved the challenge of representing nonlinear functions using only light—until now.
The team’s breakthrough begins with a special semiconductor material that responds to light. When a beam of “signal” light (carrying the input data) passes through the material, a second “pump” beam shines in from above, adjusting how the material reacts.
By changing the shape and intensity of the pump beam, the team can control how the signal light is absorbed, transmitted or amplified, depending on its intensity and the material’s behavior. This process “programs” the chip to perform different nonlinear functions.
“We’re not changing the chip’s structure,” says Feng. “We’re using light itself to create patterns inside the material, which then reshapes how the light moves through it.”
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