Running artificial intelligence applications on local devices comes with tons of advantages.

For one, you won’t require a network connection with to run voice assistant software such as Alexa on devices. Also, manufacturers can create privacy-friendly electronics that can store and process data locally.

There’s just one problem.

Current AI applications are not power-efficient enough to process data locally on smart devices. As a result, applications such as speech recognition and gesture recognition rely on cloud connection to work.

But that could change soon, thanks to a recent study from Centrum Wiskunde & Informatica (CWI).

The researchers have made a mathematical breakthrough that can make AI application a thousand times more power efficient. What’s more, they’ve made the underlying mathematical algorithms open source.

The researchers described their work in their yet to be peer-reviewed paper published in arXiv.org.

A Mathematical Breakthrough for Energy-Efficient AI Applications

Spiking neural networks have been around for a while.

However, they can be challenging to handle from a mathematical perspective. This, in turn, makes it difficult to put such a neural Network to practice.

However, such limitation didn’t stop the researchers. In the recent mathematical breakthrough, the team developed a learning algorithm for a spiking neural network.

The algorithm offers two significant advantages over current models.

• The neurons in the network communicate less frequently
• Individual neurons execute fewer calculations

Thanks to these factors, the team was able to develop a more energy-efficient AI application.

In a statement about the project, principal investigator, Sander Bohté explained:

“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.”

The breakthrough could take AI applications to the next level. For example, it becomes possible to put a more elaborate artificial intelligence in chips to enable a more extensive application.

First, new types of chips are necessary to run spiking neural networks efficiently in the real-world. Luckily, various manufacturers are already working on creating such chips.

“All kinds of companies are working hard to make this happen, like our project partner IMEC/Holst Center,” said Bohté.

Read More: Researchers Develop New Energy-Efficient AI System

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