Chip Use Light Waves to Perform Math Essential to AI Training



Chip Use Light Waves to Perform Math Essential to AI Training
Engineers have developed a new chip that uses light waves, rather than electricity, to perform the complex math essential to training AI.
Technology Briefing

Transcript


When it comes to computing, faster, better, and cheaper is always good. Now, engineers at the University of Pennsylvania have developed a new chip that uses light waves, rather than electricity, to perform the complex math essential to training AI. The chip has the potential to radically accelerate the processing speed of computers while also reducing their energy consumption. This silicon-photonic chip design is the first to bring together light — the fastest possible means of communication — with the silicon-photonic platform, which uses silicon, the cheap, abundant element used to mass-produce computer chips.

As highlighted recently in Nature Photonics, 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. The teams’ goal was to develop a platform for performing what is known as vector-matrix multiplication, a core mathematical operation required for the development and function of neural networks, which power today’s AI tools. Instead of using a silicon wafer of uniform height they made the silicon thinner 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. This allows the chip to perform mathematical calculations at the speed of light. Furthermore, constraints imposed by the commercial foundry that produced the chips, mean that this design is already ready for commercial applications, and could potentially be adapted for use in graphics processing units, for which demand has skyrocketed due to the widespread interest in developing new AI systems.

In addition to faster speed and less energy consumption, this chip has privacy advantages. That’s because many computations can happen simultaneously, meaning 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.”

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