Cornell University researchers have trained physical systems to execute generic machine learning computations, demonstrating an early but viable substitute for conventional electronic processors.
The training process enabled demonstrations with mechanical, optical, and electrical physical systems.
The mechanical system involved a titanium plate positioned atop a speaker to create a driven multimode mechanical oscillator; the optical system beamed a laser through a nonlinear crystal to convert the incoming light's colors into new colors by combining photon pairs, and the electrical system harnessed an electronic circuit with a resistor, a capacitor, an inductor, and a transistor.
The researchers fed each system pixels of an image of a handwritten number, encoded in a light pulse or an electrical voltage, and returned a similar type of optical pulse or voltage as output.
"It turns out you can turn pretty much any physical system into a neural network," said Cornell's Peter McMahon.
From Cornell Chronicle
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