# An Energy-Aware Online Learning Framework for Resource Management in Heterogeneous Platforms

arXiv: https://arxiv.org/abs/2003.09526 · HTML: https://mlsanity.com/paper/2003.09526/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Mar 2020.

## Built on by (most cited first, 3 of 3)

- [AutoFL: Enabling Heterogeneity-Aware Energy Efficient Federated Learning](https://mlsanity.com/paper/2107.08147/) (2021)
- [Preference-Aware Constrained Multi-Objective Bayesian Optimization](https://mlsanity.com/paper/2303.13034/) (2023)
- [SETGAN: Scale and Energy Trade-off GANs for Image Applications on Mobile Platforms](https://mlsanity.com/paper/2103.12896/) (2021)

## Abstract (arXiv)

Mobile platforms must satisfy the contradictory requirements of fast response time and minimum energy consumption as a function of dynamically changing applications. To address this need, system-on-chips (SoC) that are at the heart of these devices provide a variety of control knobs, such as the number of active cores and their voltage/frequency levels. Controlling these knobs optimally at runtime is challenging for two reasons. First, the large configuration space prohibits exhaustive solutions. Second, control policies designed offline are at best sub-optimal since many potential new applications are unknown at design-time. We address these challenges by proposing an online imitation learning approach. Our key idea is to construct an offline policy and adapt it online to new applications to optimize a given metric (e.g., energy). The proposed methodology leverages the supervision enabled by power-performance models learned at runtime. We demonstrate its effectiveness on a commercial mobile platform with 16 diverse benchmarks. Our approach successfully adapts the control policy to an unknown application after executing less than 25% of its instructions.