Washington DC: EPFL researchers created an algorithm that can train an analog neural network just as accurately as a digital one, allowing for the development of more efficient alternatives to power-hungry deep learning hardware.
With their ability to process vast amounts of data through algorithmic 'learning' rather than traditional programming, it often seems like the potential of deep neural networks like Chat-GPT is limitless. But as the scope and impact of these systems have grown, so have their size, complexity, and energy consumption -- the latter of which is significant enough to raise concerns about contributions to global carbon emissions.
