Stanford Researchers Cut Computing Heat Waste by More Than 60 Percent

• Stanford and SLAC researchers developed a highly precise method for measuring how electrical energy is lost as heat inside a model electronic system.
• By changing the way voltage was applied, the researchers reduced wasted energy by more than 60 percent compared with a conventional voltage pattern.
• The work could eventually help engineers develop more energy-efficient computer memory, processors, and other electronic devices at a time when AI and data centers are driving enormous growth in computing demand.

Almost every electronic device converts some of the electricity it consumes into heat. In computers, servers, smartphones, and data centers, that lost energy represents more than a thermal-management problem: it is electricity that does not contribute directly to useful computation.

Researchers at Stanford University and the Department of Energy’s SLAC National Accelerator Laboratory have now demonstrated a new way to precisely measure this energy dissipation and, more importantly, reduce it.

Using liquid crystals as a model electrical system, the research team developed a method for following energy as it moves through a device rather than simply measuring the temperature increase that occurs afterward. The researchers then used machine-learning-based optimization to determine how voltage could be applied more efficiently.

The result was a reduction in energy dissipation of more than 60 percent.

The findings, published in Physical Review Letters, could contribute to the development of computers and electronic components that perform the same operations while consuming substantially less power.

Why Computing Heat Is Such a Significant Problem

Modern computing systems contain extraordinary numbers of electronic components. Even an ordinary personal computer relies on billions of transistors repeatedly switching between electrical states.

Every time electrical signals move through these systems, some energy is dissipated.

Eventually, much of that energy becomes heat.

The problem becomes particularly important at the scale of large data centers. Artificial intelligence, cloud computing, high-performance computing, and other data-intensive technologies are increasing demand for electricity while simultaneously increasing the amount of heat that computing facilities must remove.

Cooling systems therefore consume additional electricity simply to deal with heat generated by the computing equipment.

Improving computing efficiency requires attacking the problem from both sides. Engineers can develop better cooling systems, but researchers can also attempt to prevent some of the heat from being generated in the first place.

That requires accurately determining where energy is being lost.

Measuring Energy Loss Has Been Surprisingly Difficult

Traditionally, researchers studying energy dissipation often measure temperature changes.

The concept is straightforward. If an electrical component generates heat, its temperature rises. Researchers can measure that temperature increase and estimate the amount of energy that has been dissipated.

At extremely small scales, however, the process becomes much more complicated.

Temperature changes can be extraordinarily small, while heat simultaneously moves from the component into surrounding materials. Researchers must therefore model how heat flows through the system before determining how much energy was originally dissipated.

Computing systems make the problem even harder because thousands, millions, or billions of components may interact dynamically.

Stanford professor Aaron Lindenberg, who led the research team, explained that understanding this process requires tracking the flow of energy through the system rather than relying simply on averaged measurements.

The Stanford-SLAC team therefore approached the problem differently.

Liquid Crystals Provide a Model for Complex Electronics

Instead of attempting to begin with an extraordinarily complicated computer processor, the researchers created a model system using liquid crystals.

Liquid crystals are not computers, but their behavior can reproduce some of the characteristics researchers encounter when studying complex electronic systems.

The material contains bar-shaped molecules that change their orientation when voltage is applied.

Those molecules do not necessarily move uniformly. Different regions can respond differently, creating localized domains within the material.

That behavior gives researchers a controllable system containing many interacting components.

The scientists applied changing voltage levels to the liquid crystal while simultaneously measuring electrical current.

They also observed the material through a polarized optical microscope, allowing them to watch how the molecules changed orientation as the voltage increased and decreased.

By combining the optical observations with highly precise electrical measurements, the researchers were able to determine the system’s capacitance in real time.

Capacitance describes a system’s ability to store electrical charge.

Those measurements allowed the researchers to calculate the energy dissipated as the liquid crystal moved between different states.

According to Stanford, the technique was sensitive enough that, if expressed as an equivalent temperature measurement in a perfectly thermally isolated system, it could correspond to detecting an extraordinarily small temperature rise on the nanokelvin scale.

Machine Learning Finds a More Efficient Voltage Pattern

After developing a way to precisely measure energy loss, the researchers addressed the next question: Could they reduce it?

Rather than simply applying voltage at a constant rate, the team used machine learning to optimize the shape of the voltage signal.

The system discovered that a nonlinear voltage pattern performed significantly better.

The voltage initially increased quickly, slowed briefly during a critical portion of the switching process, and then accelerated again.

This seemingly modest change produced a substantial result.

Energy dissipation fell by more than 60 percent compared with a simple linear voltage protocol.

The underlying research paper describes the optimized process as reducing dissipation by approximately a factor of three relative to the basic linear approach.

The significance extends beyond the particular liquid crystal experiment.

The research suggests that the timing and shape of electrical signals can influence how much energy a device wastes during transitions between different states.

Instead of focusing only on building components that consume less electricity, engineers may eventually be able to optimize how those components are electrically controlled.

The Next Target: Computer Memory

Liquid crystals provided researchers with a controllable experimental platform, but they are not the ultimate objective.

The Stanford-SLAC team plans to apply the technique to ferroelectric materials.

Ferroelectric devices are particularly interesting because they can be used in computer memory and other electronic systems. They can switch quickly while requiring relatively little energy, making them candidates for future low-power computing technologies.

Studying energy dissipation directly inside these materials could therefore bring the research much closer to practical computing applications.

First author Yuejun Shen said the ultimate objective is to develop methods capable of measuring and optimizing dissipation across many different kinds of electrical devices.

If researchers can develop such broadly applicable techniques, chip designers could potentially optimize energy efficiency at the component and switching level rather than relying primarily on improvements in cooling or power delivery.

Why the Research Matters for AI and Data Centers

The timing of the research is particularly important.

Artificial intelligence has dramatically increased demand for high-performance computing infrastructure. Large AI models require significant computing power for both training and operation, creating growing concerns about electricity consumption, grid capacity, cooling requirements, and environmental impact.

Recent research on AI sustainability has increasingly focused on improving hardware efficiency alongside renewable energy, cooling technology, resource management, and more efficient AI algorithms.

Reducing energy losses inside electronic components attacks the problem closer to its source.

Every watt of electricity that performs useful computational work rather than immediately becoming waste heat potentially creates two advantages.

First, the computing system requires less electricity.

Second, the cooling infrastructure may have less heat to remove.

At data-center scale, even relatively small efficiency improvements can become important when multiplied across thousands of servers operating continuously.

A technology capable of producing substantial reductions in component-level dissipation could therefore influence both computing performance and energy infrastructure.

From Measuring Waste Heat to Preventing It

Perhaps the most important aspect of the Stanford-SLAC research is that it combines measurement with optimization.

Researchers did not simply demonstrate that electrical devices lose energy. That phenomenon has been understood for decades.

Instead, they developed a way to observe energy dissipation with unusually high precision and then used those measurements to identify a control method that significantly reduced the loss.

The approach could eventually allow engineers to treat energy dissipation as something that can be actively optimized rather than simply accepted as an unavoidable byproduct of computation.

Moving from liquid crystals to actual computing materials will determine how broadly the technique can be applied.

However, if similar reductions can eventually be achieved in memory, processors, or other electronic systems, the implications could extend from individual devices to some of the world’s largest computing facilities.

As computing demand continues to grow, particularly because of AI, preventing energy from becoming waste heat may become just as important as finding better ways to remove that heat after it has already been generated.

Original Article

Stanford Report — “Researchers cut computing heat waste by 60 percent”
Published September 8, 2026
Read the original Stanford article

Further Reading

Physical Review Letters — “Quantifying and Minimizing Dissipation in a Nonequilibrium Phase Transition”
The peer-reviewed research paper behind the Stanford report, published August 12, 2026.
Read the Physical Review Letters paper

Nature Reviews Clean Technology — “Strategies and design for increasing AI sustainability”
A 2026 review examining hardware, data centers, energy consumption, emissions, water use, and other sustainability challenges associated with expanding AI infrastructure.
Read the Nature review

Nature Reviews Materials — “Engineered interfaces in electronic materials for energy-efficient computing”
Examines materials and interfaces that could help reduce the energy requirements of future computing technologies.
Read the Nature Reviews Materials article

IEEE Transactions on Sustainable Computing — “Thermal Modeling and Thermal-Aware Energy Saving Methods for Cloud Data Centers”
A broader review of thermal management, cooling optimization, workload scheduling, and energy reduction in cloud data centers.
Read the IEEE research overview

Nature Electronics — “Energy-efficient computing at cryogenic temperatures”
Explores alternative approaches to reducing the energy requirements of high-performance and data-intensive computing.
Read the Nature Electronics article

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