| Date | 28th, Oct 2022 |
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Artificial-intelligence systems are increasingly limited by the hardware used to implement them. Now comes a new superconducting photonic circuit that mimics the links between brain cells—burning just 0.3 percent of the energy of its human counterparts while operating some 30,000 times as fast.
In artificial neural networks, components called neurons are fed data and cooperate to solve a problem, such as recognizing faces. The neural net repeatedly adjusts the synapses—the links between its neurons—and determines whether the resulting patterns of behavior are better at finding a solution. Over time, the network discovers which patterns are best at computing results. It then adopts these patterns as defaults, mimicking the process of learning in the human brain.
Although AI systems are increasingly finding real-world applications, they face a number of major challenges, given the hardware used to run them. One solution that researchers have investigated to solve this problem is to develop brain-inspired “neuromorphic” computer hardware.
“When I look around at all the concepts that have been unearthed, I really feel like we’re onto something.”—Jeffrey Shainline, NIST
For instance, neuromorphic microchip components may “spike,” or generate an output signal, only after they receive a certain amount of input signals over a given time, a strategy that more closely mimics how real biological neurons behave. By only rarely firing spikes, these devices shuffle around much less data than typical artificial neural networks and, in principle, require much less power and communication bandwidth.
However, neuromorphic hardware typically uses conventional electronics, which ends up limiting their complexity and speed. For example, biological neurons can each possess tens of thousands of synapses, but neuromorphic devices struggle to connect their artificial neurons to a few others. One solution is multiplexing, in which a single data channel may carry many signals at the same time. However, as chips become larger and more intricate, computations may slow down.
In a new study, researchers explored using optical transmitters and receivers to connect neurons instead. Optical links, or waveguides, can in principle connect each neuron with thousands of others at light-speed communication rates.
The scientists used superconducting nanowire devices capable of detecting single photons. These optical signals are the smallest possible, constituting the physical limit of energy efficiency.
