Communications of the ACM
Thermodynamics Could Be the Future of Computing, Researchers Say
Today's computing systems are hitting limits that are related to thermodynamics. Energy consumption is one issue. Small components in computers aren't stable because of thermodynamic fluctuations. Code is becoming increasingly more difficult to organize.
"It all goes back to thermodynamics," says Todd Hylton, a professor of practice at the University of California San Diego and executive director of its Contextual Robotics Institute. "That's where we should be looking for answers."
As Moore's Law reaches its limits, thermodynamic computing might prove to be the future of the field, says a new report, "Thermodynamic Computing," from an international team of 38 researchers led by Hylton.
"If we want to make computers function more efficiently then we should care about energy and its ability to efficiently create state changes — i.e. we should care about thermodynamics," the researchers write.
Thermodynamics drives organization in the real world, so it should drive technology too, Hylton says. "Right now, we are fighting against thermodynamics in our technology."
What would a thermodynamic computer look like? An "engineered, multi-scale, complex system that, when exposed to external potentials (inputs), spontaneously transitions between states in the short term while refining its organization in the long term, both as part of an inherent, adaptive process driven by thermodynamics," the researchers write.
Such a system could make computing much more efficient and cheaper, lowering the environmental impact of computing systems. In the long term, it could enable "understanding of the organization and computational power of living systems, potentially including the spontaneous emergence of 'intelligence,'" researchers write, as well as "a very large increase in the capabilities of small, low-cost computing systems, such as perceptual capabilities that rival those of animal sensory systems." In the short term, findings could be applied to improve machine learning.
The report calls for:
- Core research to better understand thermodynamics in the context of computing;
- Building simulations and proof of concept systems to show what thermodynamic computing might look like;
- Research on the foundational components of thermodynamic computing.
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