Plus the artificial synapses can fire a billion times per second, compared to just 50 times a second for real brain synapses. “By having this efficiency, you open up the possibility of tackling more complex problems,” Schneider said. “You can make larger system without needing your own giant power station.”
The NIST synapse is a type of Josephson Junction, a sandwich of two superconductors around an insulating layer. Josephson Junctions have been around for a while, but makes these synapses special is that the insulating layer is packed with special magnetic clusters that allow the researchers to control how much energy is required to throw the switch, known as the critical current.
The magnetic clusters are “tunable” because they each have a particular spin, similar to magnetic orientation. When all of the clusters are oriented in different directions, the critical current is high. Using pulses from a magnetic field, though, the researchers can align the clusters’ orientation, lowering the critical current.
This tuning process is essential because it replicates the natural “learning” process of real synapses, in which frequent interactions result in a lower critical current. For now, Schneider and his team will tune the synapses manually to achieve greater efficiencies for neural network algorithms, but the ultimate goal is for the synapses to tune themselves organically through the frequency and quality of interactions within the circuit.
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So how long until we have AI applications running on neuromorphic circuits? Sooner than you think. Schneider said most of the large-scale fabrication issues around manufacturing chips with millions or billions of artificial synapses have already been worked out by other researchers trying to make computers based on digital Josephson Junctions.
“We’re optimistic that we can start to scale these devices somewhat aggressively,” said Schneider, who puts the figure at between five and 10 years.
One drawback of the low-power artificial synapses is that they only work at 4 Kelvin (-452 F). That’s not a problem for applications hosted in data centers, where liquid-helium cryocooling is an option. But don’t expect to see neuromorphic processors in your cell phone or self-driving car.