Revisiting "Cooler is Better": ITD-Aware Per-CPU Thermal Optimization for Sustainable Data Center Operation

2026-06-10

Authors: Jason Crop, Hayden Moore, Sudeep Pasricha

ArXiv: 2606.11163v1

PDF: Download PDF

For decades, data center operators have lived by a simple rule: cooler chips are more efficient chips. Crank up the AC, blast cold air across the servers, and you'll save power because cooler transistors leak less current. This intuition shaped billions of dollars worth of cooling infrastructure. This paper says that rule is now wrong for modern low-voltage CPUs — and acting on the outdated assumption is quietly wasting energy at exactly the moment when AI workloads are pushing data centers toward grid-level power limits.

The villain here is a phenomenon called inverse temperature dependence (ITD). In older chips running at higher voltages, transistors got slower as they heated up, so cooling helped them run faster and use less power. But modern CPUs operate at very low supply voltages to save energy, and at those voltages the physics flips: transistors actually get slower when they're cold. To keep performance up, the chip has to raise its supply voltage when it's running cool. And since power scales roughly with the square of voltage, that voltage bump can wipe out the savings you got from reducing leakage.

The result is a non-monotonic curve for performance-per-watt. Instead of "colder = better forever," efficiency now peaks at some intermediate temperature — neither hot nor cold, but a sweet spot somewhere in the middle. Run too cold, and you waste power on overcooling plus a forced voltage hike. Run too hot, and leakage current dominates. The optimum sits in between, and crucially, it's different for every individual chip because of manufacturing variation.

The authors propose a per-CPU thermal optimization scheme that:

The practical upshot: data centers could meaningfully cut both compute power and cooling power simultaneously, without buying new hardware. They just need to stop chasing "as cold as possible" and start tuning each chip to its actual physics.

Why it matters: As AI workloads push data centers against grid power limits, this work shows that a decades-old cooling assumption is now leaving real efficiency on the table — and the fix is software and policy, not new silicon.

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