Optical Tech Would Update a Robot's AI on the Fly
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.
Key Takeaways
- 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 receiver here is doing something different: Directly altering its own memory using the photocurrents produced by the beamed array of light.
- 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.
- "It's got massive commercial implications.
- The transmitter beams the data to the array of SRAM cells, which in this case are modified to contain photodiodes.
Light hitting each photodiode creates a current to flip binary values in the SRAM.
- The transmitter I saw in He and Seo's lab is only a proof of concept, emitting a static 14x14-bit matrix through a metal mask over the light.
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 ).
For more details please read the original article at IEEE Spectrum AI.
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