# Modeling the Temperature Bias of Power Consumption for Nanometer-Scale CPUs in Application Processors

arXiv: https://arxiv.org/abs/1404.3381 · HTML: https://mlsanity.com/paper/1404.3381/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Apr 2014.

## Built on by (most cited first, 1 of 1)

- [LEAF + AIO: Edge-Assisted Energy-Aware Object Detection for Mobile Augmented Reality](https://mlsanity.com/paper/2205.13770/) (2022)

## Abstract (arXiv)

We introduce and experimentally validate a new macro-level model of the CPU temperature/power relationship within nanometer-scale application processors or system-on-chips. By adopting a holistic view, this model is able to take into account many of the physical effects that occur within such systems. Together with two algorithms described in the paper, our results can be used, for instance by engineers designing power or thermal management units, to cancel the temperature-induced bias on power measurements. This will help them gather temperature-neutral power data while running multiple instance of their benchmarks. Also power requirements and system failure rates can be decreased by controlling the CPU's thermal behavior. Even though it is usually assumed that the temperature/power relationship is exponentially related, there is however a lack of publicly available physical temperature/power measurements to back up this assumption, something our paper corrects. Via measurements on two pertinent platforms sporting nanometer-scale application processors, we show that the power/temperature relationship is indeed very likely exponential over a 20degC to 85degC temperature range. Our data suggest that, for application processors operating between 20degC and 50degC, a quadratic model is still accurate and a linear approximation is acceptable.