- Thermodynamic computing uses physical energy flows instead of fixed digital circuits to perform AI calculations
- Image data is allowed to degrade naturally through small fluctuations in computer components.
- Expanding to complex imaging will require entirely new hardware designs and approaches
Scientists are exploring a new type of computing that uses natural energy flows to 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 much less power than current systems.
How Thermodynamic Imaging Works
The thermodynamic imaging process is unusual compared to normal computing. It begins when the computer receives a set of images, which it then allows to “degrade.”
In this context, downgrading does not mean that images are removed or damaged; It means that image data can spread or change naturally due to small fluctuations in the system.
These fluctuations are caused by physical energy moving through the computer components, such as small currents and vibrations.
Over time, these interactions cause images to become blurry or noisy, creating a kind of natural clutter; The system then measures the probability of reversing this disorder, adjusting its internal configuration 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 that conventional computers use.
Stephen Whitelam, a researcher at Lawrence Berkeley National Laboratory, has shown that thermodynamic computing can produce simple images, such as handwritten digits.
These results are much simpler than those from AI imagers like DALL-E or Google Gemini’s Nano Banana Pro.
Still, research shows that physical systems can perform basic machine learning tasks, showing a new way AI could work.
However, expanding this approach to produce high-quality, full-featured images will require new types of hardware.
Proponents claim that thermodynamic computing could reduce the energy needed for AI imaging by a factor of ten billion compared to standard computers.
If successful, this would greatly reduce the power consumption of data centers running AI models.
Although the first thermodynamic computing chip has been manufactured, current prototypes are basic and cannot match conventional AI tools.
The researchers stress that the concept is limited to basic principles and that practical implementations will require advances in both hardware and computational design.
“This research suggests that it is possible to make hardware to perform certain types of machine learning… at considerably lower energy costs than today,” Whitelam said. IEEE.
“We don’t yet know how to design a thermodynamic computer that is as good at imaging as, say, DALL-E…we will still need to figure out how to build the hardware to do this.”
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