Optical Memory Link Could Boost AI In Robotics

Atop a lab bench, Cornell Tech postdoctoral researcher Yifan He positions the lens of an optical receiver almost a meter away from an LED emitting a beam of red light. The computer monitor attached to the receiver takes a beat to refresh, then displays an array of squares that resemble a QR code.

When you hold your phone camera up to a QR code, light strikes the image sensor as only a first step to revealing the data hidden behind the black and white matrix. The receiver here is doing something different: Directly altering its own memory using the photocurrents produced by the beamed array of light. And unlike the data behind a QR code, which might point to a simple web address, this optical code could convey the parameters of an AI model.

The new receiver design, presented last month at the IEEE/JSAP Symposium on VLSI Technology & Circuits, seeks to reduce the burden of increasing memory demands on AI systems. Shining data down onto processors could lower the energy typically required for data centers, self-driving cars, and even “edge” applications like AI-powered robots, researchers say.

“People are designing all sorts of different AI chips,” says Jae-sun Seo, an associate professor of electrical and computer engineering at Cornell Tech, in New York City. These processors don’t often have room for all the parameters that make up AI models, so the additional data is stored in dynamic random-access memory (DRAM). The electrical connections commonly used to move the data between the DRAM and the processor create cost and efficiency concerns when systems scale up. “That’s one of the major bottlenecks.”

Optical links move data at high bandwidth with less energy loss than metal wires, but today’s optical receivers undercut that advantage by relying on power-hungry analog circuits to convert light to electronic bits. The group’s new tech would instead receive rapid flashes of digital QR code-like matrices so that chips can tweak model parameters without those analog circuits, enabling fully digital optical communication that would consume less energy.

“This is a really important problem,” says Dennis Sylvester, an IEEE Fellow who chairs the University of Michigan’s electrical and computer engineering department and was not involved in the work. “It’s got massive commercial implications. This solution is a clever way of dealing with it.”

Jae-sun Seo [left] and Yifan He [right] have developed a receiver that can edit memory in response to QR-code-like arrays of light.Alex Music

How light “flips” memory to power AI

Processors have a bit of built-in static random-access memory (SRAM), but not enough to allow an AI model to run independently. While SRAM is the faster of the two memory options, DRAM can store more data in the same footprint.

In the new system, the DRAM sits with the transmitter and the receiver is part of the processor’s SRAM. The transmitter beams the data to the array of SRAM cells, which in this…