- Thermodynamic computing uses physical energy flows instead of fixed digital circuits to perform AI calculations
- Image data is allowed to degrade naturally through tiny fluctuations in the computer’s components
- Scaling to complex image generation will require entirely new hardware designs and approaches
Scientists are exploring a new type of computing which uses natural energy flows to potentially perform AI tasks more efficiently.
Unlike traditional digital computers, which rely on fixed circuits and exact calculations, thermodynamic computing works with randomness, noise, and physical interactions to solve problems.
The idea is that this method could allow AI tools, including image editors, to run using far less power than current systems.
How thermodynamic image generation works
The process of thermodynamic image generation is unusual compared with normal computing. It begins with the computer receiving a set of images, which it then allows to “degrade.”
In this context, degrade does not mean the images are deleted or damaged; it means the data in the images is allowed to spread or change naturally due to tiny fluctuations in the system.
These fluctuations are caused by the physical energy moving through the computer’s components, like tiny currents and vibrations.
Over time, these interactions cause the images to become blurred or noisy, creating a kind of natural disorder – then, the system measures the likelihood of reversing this disorder, adjusting its internal settings to make reconstruction more likely.
By running this process many times, the computer gradually restores the original images without following the step-by-step logic used by conventional computers.
Stephen Whitelam, a researcher at Lawrence Berkeley National Laboratory, has demonstrated that thermodynamic computing can produce simple images such as handwritten digits.
These outputs are far simpler than those from AI image generators like DALL-E or Google Gemini’s Nano Banana Pro.
Still, the research proves that physical systems can perform basic machine learning tasks, showing a new way AI could work.
However, scaling this approach to produce high-quality, fully featured images will require new types of hardware.
Proponents claim that thermodynamic computing could reduce the energy needed for AI image generation by a factor of ten billion compared with standard computers.
If successful, this would greatly reduce the energy consumption of data centers running AI models.
Although the first thermodynamic computing chip has been made, current prototypes are basic and cannot match mainstream AI tools.
Researchers stress that the concept is limited to basic principles, and practical implementations will require breakthroughs in both hardware and computational design.
“This research suggests that it’s possible to make hardware to do certain types of machine learning…with considerably lower energy cost than we do at present,” Whitelam told IEEE.
“We don’t yet know how to design a thermodynamic computer that would be as good at image generation as, say, DALL-E…it will still be necessary to work out how to build the hardware to do this.”
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