In the age of artificial intelligence, large-scale neural networks seem to dominate the scene. However, a major challenge persists: their energy efficiency. As the world continues to marvel at the prowess of AI, researchers are examining how to make these networks as energy efficient as human brains.
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How do neural networks learn?
Neural networks are computational models inspired by the functioning of biological brains. They are made up of numerous “nodes” which carry out calculations, similar to the role of neurons.
These networks learn autonomously from the data they receive. For example, by exposing a network to a multitude of images, it gains the ability to categorize them and recognize their content without explicit instructions.
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The paradox of overparameterized networks
In the world of AI, the larger a network is, i.e. the more parameters it has, the more precisely it can perform complex tasks. This approach, known as the “lottery ticket hypothesis,” requires considerable computing resources, however.
As a result, the energy consumption of these networks becomes a significant problem, especially as the demand for computing power increases. Finding a way to reduce this consumption while maintaining network efficiency is crucial.
Curriculum learning: a promising approach
To meet this challenge, scientists are taking inspiration from biological brains, which, although limited in resources, perform incredibly complex tasks. One hypothesis put forward is that the order of learning could be the key.
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By presenting training data to machines in increasing order of difficulty, a method called “curriculum learning,” it might be possible to reduce the resources needed.
| 🔍 Approach | Curriculum learning could optimize learning using fewer resources |
A path to energy efficiency
Although curriculum learning appears promising, studies have shown that for highly overparameterized networks, this approach does not improve performance during the training phase.
However, this does not mean that this method is useless. By adjusting the initial size of the networks, it might be possible to combine curriculum learning and energy efficiency.
The researchers are pursuing this avenue, exploring how smaller networks can benefit from structured learning to improve their performance while saving energy. This quest raises a central question: how can we truly bring technology closer to the natural efficiency of biological brains?
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