Breakthrough in energy efficient Artificial Intelligence
Thanks to a mathematical breakthrough, AI applications like speech recognition, gesture recognition and ECG classification can become a hundred to a thousand times more energy efficient. This means it will be possible to put much more elaborate AI in chips, enabling applications to run on a smartphone or smartwatch where before this was done in the cloud.
Running the AI on local devices makes the applications more robust and privacy-friendly: robust, because a network connection with the cloud is no longer necessary. And more privacy friendly because data can be stored and processed locally.
The mathematical breakthrough has been achieved by researchers of Centrum Wiskunde & Informatica (CWI), the Dutch national research center for mathematics and computer science together with the IMEC/Holst Research Centre from Eindhoven. The results have been published in a paper (by Bojian Yin, Federico Corradi, and Sander M. Bohté) of the International Conference on Neuromorphic Systems. The underlying mathematical algorithms have been made available open source.
Under supervision of CWI researcher and UvA professor cognitive neurobiology Sander Bohté, researchers developed a learning algorithm for so-called spiking neural networks. Such networks have been around for some time, but are very difficult to handle from a mathematical perspective, making it hard to put them into practice so far. The new algorithm is groundbreaking in two ways: the neurons in the network are required to communicate a lot less frequently, and each individual neuron has to execute fewer calculations.
“The combination of these two breakthroughs make AI algorithms a thousand times more energy efficient in comparison with standard neural networks, and a factor hundred more energy efficient than current state-of-the-art neural networks”, says principal investigator Sander Bohté.
You can read more about this breakthrough on the CWI website.
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