MIT Engineers Design Structures That Compute With Heat

Tiny silicon structures could enable more energy-efficient computation by using excess heat instead of electricity.

Jan. 29, 2026 at 11:47pm

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MIT researchers have designed silicon structures that can perform calculations in an electronic device using excess heat instead of electricity. These tiny structures could someday enable more energy-efficient computation by encoding data as temperatures and using heat flow to perform matrix calculations.

Why it matters

This computing method could eliminate the need for multiple temperature sensors on chips and enable more energy-efficient ways to detect heat sources and measure temperature changes in electronics without consuming extra power. While scaling up the technique for modern deep-learning models remains a challenge, it could have immediate applications in thermal management and heat source detection in microelectronics.

The details

The researchers used a software system they previously developed to automatically design complex silicon structures, each roughly the size of a dust particle, that can perform computations using heat conduction. These structures encode data as a set of temperatures and use the flow and distribution of heat to perform matrix vector multiplication with over 99% accuracy. However, the researchers still need to overcome challenges to scale up the technique for large-scale applications like deep learning, as the structures become less accurate with more complicated matrices.

  • The research appears today in Physical Review Applied.

The players

Caio Silva

An undergraduate student in the Department of Physics and lead author of the paper on the new computing paradigm.

Giuseppe Romano

A research scientist at MIT's Institute for Soldier Nanotechnologies and a member of the MIT-IBM Watson AI Lab, and the senior author of the paper.

MIT

The university where the researchers are based and where the research was conducted.

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What they’re saying

“Most of the time, when you are performing computations in an electronic device, heat is the waste product. You often want to get rid of as much heat as you can. But here, we've taken the opposite approach by using heat as a form of information itself and showing that computing with heat is possible.”

— Caio Silva, Undergraduate student

“Finding the right topology for a given matrix is challenging. We beat this problem by developing an optimization algorithm that ensures the topology being developed is as close as possible to the desired matrix without having any weird parts.”

— Caio Silva, Undergraduate student

“These structures are far too complicated for us to come up with just through our own intuition. We need to teach a computer to design them for us. That is what makes inverse design a very powerful technique.”

— Giuseppe Romano, Research scientist

What’s next

Building on this proof-of-concept, the researchers want to design structures that can perform sequential operations, where the output of one structure becomes an input for the next. They also plan to develop programmable structures, enabling them to encode different matrices without starting from scratch with a new structure each time.

The takeaway

This novel computing method using heat conduction could lead to more energy-efficient ways to detect heat sources and measure temperature changes in electronics, eliminating the need for multiple temperature sensors. While scaling up the technique for modern deep learning remains a challenge, it represents an innovative approach to computing that leverages the ubiquitous presence of heat in electronic devices.