The human brain processes information in an incredibly energy-efficient way. Its power consumption is only a tiny 20 watts. Computers that mimic the brain's neural networks via deep learning have given rise to wonderful applications in recent years, but they consume much more energy than the human brain.
Thanks to an algorithmic breakthrough in training spiking neural networks (SNNs), many applications of artificial intelligence, such as speech recognition, gesture recognition, and the classification of electrocardiograms (ECGs) can be made more energy-efficient by a factor of 100 to 1,000. This will make it possible to put much more artificial intelligence (AI) into chips, allowing applications to run on a smartwatch or a smartphone, for example, which until now had to be done in the cloud.
Read the full interview by Bennie Mols on the ACM Communications website
