Deployed for AI, e-prop would require only 20 watts, approximately one-millionth the energy a supercomputer uses.

Artificial intelligence models continue to grow in sophistication and complexity, adding to the need for more data, computation, and energy.

To help combat increasing energy costs, researchers at TU Graz’s Institute of Theoretical Computer Science have developed a new algorithm, called e-propagation (e-prop for short).

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E-prop mimics how neurons send electrical impulses to other neurons in our brain, which massively reduces the amount of energy human brains use, in comparison to machine learning. Deployed for AI, e-prop would require only 20 watts, approximately one-millionth the energy a supercomputer uses.

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