Spiking Neural Networks requires less frequency while communicating, and involves minimum calculations for performing the task.

The neural networks are the brain of Artificial Intelligence. Just like the neurons in the human body, these neural networks precede every process of AI. The modern neural networks are efficient in performing tasks but are lacks energy efficiency. That’s why performing tasks like speech recognition, ECG and gesture recognition entails consumption of extensive energy. Moreover, with the amount of energy these neural networks consume, running AI applications on mobile phones or chips or smartwatch without cloud-intervention becomes a challenging task. Despite their effectiveness, this becomes a limiting factor in their use.

To outsmart such challenge, the researchers at Centrum Wiskunde & Informatica (CWI) the Dutch national research centre for mathematics and computer science, collaborated with the IMEC/Holst Research Center from Eindhoven, Netherlands. They formulated a mathematical breakthrough that would assist in energy-efficient neural networks. The researchers have developed a deep learning algorithm known as Spiking Neural Networks that requires less frequency while communicating and involves minimum calculations for performing the task.

The principal investigator Sander Bohté of this research project has said: “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 outcome of this mathematical breakthrough is published in a paper called, ‘Effective and Efficient Computation with Multiple-Timescale Spiking Recurrent Neural Network’. In this article, we will observe the key points of the research paper.

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Unravelling the Breakthrough in Energy Efficient Artificial Intelligence
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