New Austrian ‘e-prop’ Learning Algorithm Could Expand AI Applications

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“The high energy consumption of artificial neural networks’ learning activities is one of the biggest hurdles for the broad use of Artificial Intelligence (AI), especially in mobile applications. One approach to solving this problem can be gleaned from knowledge about the human brain. Although it has the computing power of a supercomputer, it only needs 20 watts, which is only a millionth of the energy of a supercomputer. One of the reasons for this is the efficient transfer of information between neurons in the brain. Neurons send short electrical impulses (spikes) to other neurons – but, to save energy, only as often as absolutely necessary …

With many of the machine learning techniques currently in use, all network activities are stored centrally and offline in order to trace every few steps how the connections were used during the calculations. However, this requires a constant data transfer between the memory and the processors – one of the main reasons for the excessive energy consumption of current AI implementations. e-prop, on the other hand, works completely online and does not require separate memory even in real operation – thus making learning much more energy efficient.”

— New learning algorithm should significantly expand the possible applications of AI, Human Brain Project