Researchers at the Max Planck Institute for the Science of Light have developed a simpler and potentially more sustainable method for implementing neural networks using optical systems. A new optical system for neural networks has been developed by the Max Planck Institute, offering a simpler and more energy-efficient alternative to current methods. This system uses light transmission to perform computations, reducing the complexity and energy demands associated with traditional neural networks.
Optical Neural Networks Scientists propose a new way of implementing a neural network with an optical system which could make machine learning more sustainable in the future. The researchers at the Max Planck Institute for the Science of Light have published their new method in Nature Physics , demonstrating a method much simpler than previous approaches. Machine learning and artificial intelligence are becoming increasingly widespread with applications ranging from computer vision to text generation, as demonstrated by ChatGPT.
However, these complex tasks require increasingly complex neural networks; some with many billion parameters. This rapid growth of neural network size has put the technologies on an unsustainable path due to their exponentially growing energy consumption and training times. For instance, it is estimated that training GPT-3 consumed more than 1,000 MWh of energy, which amounts to the daily electrical energy consumption of a small town.
This trend has created a .
