An algorithm to train an analog neural network as a digital one
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Training algorithm breaks barriers to deep physical neural networks
With their ability to process vast amounts of data through algorithmic ‘learning’ rather than traditional programming, it often seems like the potential of deep neural networks like Chat-GPT is limitless. But as the scope and impact of these systems have grown, so have their size, complexity, and energy consumption – the latter of which is significant enough to raise concerns about contributions to global carbon emissions.
And while we often think of technological advancement in terms of shifting from analog to digital, researchers are now looking for answers to this problem in physical alternatives to digital deep neural networks. One such researcher is Romain Fleury of EPFL’s Laboratory of Wave Engineering in the School of Engineering. In a paper published in Science, he and his colleagues describe an algorithm for training physical systems that shows improved speed, enhanced robustness, and reduced power consumption compared to other methods.
“We successfully tested our training algorithm on three wave-based physical systems that use sound waves, light waves, and microwaves to carry information, rather than electrons. But our versatile approach can be used to train any physical system,” says first author and LWE researcher Ali Momeni.
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