A mind-bending new report claims that 'thermodynamic computing' could, in theory, drastically reduce the energy consumed by AI to generate images, using just one ten-billionth of the energy of current popular tools. As reported by IEEE Spectrum, two recent studies hint at the potential of this burgeoning technology, but its proponents admit the solution is rudimentary.
According to the report, Lawrence Berkeley National Laboratory staff scientist Stephen Withelam claims thermodynamic computing could be used for AI image generation "with a much lower energy cost than current digital hardware can." In a January 10 article published by Whitelam and Corneel Casert, also of Berkeley, the pair outlined how "it was possible to create a thermodynamic version of a neural network," laying the foundations for generating images using thermodynamic computing.
