Problem scaling AI? MIT proposes sub

MIT scientists used special photodetectors to perform an AI equation just by beaming light to a low-power client device. The approach could be especially useful in deep space. 

Tiernan Ray/ZDNET

One of the most pressing concerns for the industrial application of artificial intelligence is how to run the programs on small computing devices that have very little processing power, very little memory, and possibly a limit in terms of energy available, in the case of batteries.

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The so-called edge market for AI has been a huge area of late, with startups receiving tens of millions in venture capital to come up with chips and software. The edge effort has led to special development tools for machine-learning forms of AI, such as the TinyML initiative from Google. 

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"This result may at first seem surprising given that less than a single photon per MAC is counterintuitive," wrote Sludds and team. "We can understand this measurement better by noting that at readout, we have performed a vector-vector product with M = 100 MACs. Each MAC can have less than a single photon in it, but the measured signal will have many photons in it."

The implications for computing could be profound. 

"The realization of computing with less than one photon per MAC," they wrote, "could enable a new class of computing systems that protect both client input and server weight data" from the standpoint of data privacy. It could also make computing on spacecraft more reliable. "Weight data from a directional base station could be transmitted to the spacecraft and classified on the craft, before the results are transmitted to Earth."

All the parts of Netcast can be made today in any standard semiconductor chip factory, Sludds and team noted.

In concluding, they wrote, "Our approach removes a fundamental bottleneck in edge computing, enabling high-speed computing on deployed sensors and drones."

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