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UPenn develops optical chip to process complex math for AI

21 Feb 2024

Offers potential to accelerate processing speed, while cutting energy need.

Engineers at the University of Pennsylvania (UPenn) have developed a new optical chip to perform the complex math essential to training AI. Its developers say that the chip has the potential “to radically accelerate the processing speed of computers while also reducing their energy consumption”.

The silicon photonics-based (SiPh) chip’s design brings together Benjamin Franklin Medal Laureate and H. Nedwill Ramsey Professor Nader Engheta’s pioneering research in manipulating materials at the nanoscale to perform mathematical computations using light with the SiPh platform.

The UPenn statement says, “the interaction of light waves with matter represents one possible avenue for developing computers that supersede the limitations of today’s chips, which are essentially based on the same principles as chips from the earliest days of the computing revolution in the 1960s”.

In a paper in Nature Photonics, Engheta’s group, together with that of Firooz Aflatouni, Associate Professor in Electrical and Systems Engineering, describes the development of the new chip. “We decided to join forces,” said Engheta, leveraging the fact that Aflatouni’s research group has pioneered nanoscale silicon devices.

Their goal was to develop a platform for performing what is known as vector-matrix multiplication, a core mathematical operation in the development and function of neural networks, the computer architecture that powers today’s AI tools.

‘Make the silicon thinner’

Instead of using a silicon wafer of uniform height, Engheta said. “You make the silicon thinner, say 150 nanometers,” but only in specific regions. Those variations in height — without the addition of any other materials — provide a means of controlling the propagation of light through the chip, since the variations in height can be distributed to cause light to scatter in specific patterns, allowing the chip to perform mathematical calculations at the speed of light.

Due to the constraints imposed by the commercial foundry that produced the chips, Aflatouni said, this design is already ready for commercial applications, and could potentially be adapted for use in graphics processing units (GPUs), the demand for which has skyrocketed with the widespread interest in developing new AI systems.

“They can adopt the silicon photonics platform as an add-on,” said Aflatouni, “and then you could speed up training and classification.”

In addition to faster speed and less energy consumption, Engheta and Aflatouni’s chip has privacy advantages: because many computations can happen simultaneously, there will be no need to store sensitive information in a computer’s working memory, rendering a future computer powered by such technology virtually unhackable. “No one can hack into a non-existing memory to access your information,” said Aflatouni.

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