MEMCOP: memory-aware co-operative power management governor for mobile games

Hsieh, Chen-Ying; Park, Jurn-Gyu; Dutt, Nikil; Lim, Sung-Soo

doi:10.1007/s10617-018-9201-8

Cited by 4 publications

(1 citation statement)

References 16 publications

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“…A conventional DVFS governor cannot detect these memory utilization changes by sensing utilization, causing prediction errors to increase. MEMCOP, a Memory-aware Cooperative Power Management Governor for Mobile games [5], senses the number of last level cache misses to monitor the memory pressure of the system in addition to CPU, and GPU memory utilization. This prevents the CPU DVFS governor from increasing frequency due to inaccurate predictions caused by variation in memory access time.…”

Section: Sensors To Capture Dynamismmentioning

confidence: 99%

Reflecting on Self-Aware Systems-on-Chip

Donyanavard

Mück

Moazzemi

et al. 2020

A Journey of Embedded and Cyber-Physical Systems

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Introduction to Self-Aware Systems-on-ChipWe are seeing an increasing number of complex cyber-physical systems (CPS) deployed for various applications, such as road-traffic control involving communicating autonomous cars and infrastructure, or smart grids controlling energy delivery down to the individual device. These distributed applications follow common design objectives, such as energy-efficiency, and require guarantees for high availability, real time or safety. In this context, autonomy is crucial: multiple system goals varying over time need to be adaptively managed and objectives holistically coordinated. By empowering future CPS with self-awareness, these systems promise to dynamically adapt, learn, and manage unforeseen changes [6].

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Section: Sensors To Capture Dynamismmentioning

confidence: 99%

Reflecting on Self-Aware Systems-on-Chip

Donyanavard

Mück

Moazzemi

et al. 2020

A Journey of Embedded and Cyber-Physical Systems

Self Cite

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Memory-Aware DVFS Governing Policy for Improved Energy-Saving in the Linux Kernel

Shin,

Kim,

Hong

2023

2023 IEEE 29th International Conference on Embedded and Real-Time Computing Systems and Applications (RTCSA)

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An Interpretable Machine Learning Model Enhanced Integrated CPU-GPU DVFS Governor

ParkJurn-Gyu

Dutt

LimSung-Soo

2021

ACM Trans. Embed. Comput. Syst.

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Modern heterogeneous CPU-GPU-based mobile architectures, which execute intensive mobile gaming/graphics applications, use software governors to achieve high performance with energy-efficiency. However, existing governors typically utilize simple statistical or heuristic models, assuming linear relationships using a small unbalanced dataset of mobile games; and the limitations result in high prediction errors for dynamic and diverse gaming workloads on heterogeneous platforms. To overcome these limitations, we propose an interpretable machine learning (ML) model enhanced integrated CPU-GPU governor: (1) It builds tree-based piecewise linear models (i.e., model trees) offline considering both high accuracy (low error) and interpretable ML models based on mathematical formulas using a simulatability operation counts quantitative metric. And then (2) it deploys the selected models for online estimation into an integrated CPU-GPU Dynamic Voltage Frequency Scaling governor. Our experiments on a test set of 20 mobile games exhibiting diverse characteristics show that our governor achieved significant energy efficiency gains of over 10% (up to 38%) improvements on average in energy-per-frame with a surprising-but-modest 3% improvement in Frames-per-Second performance, compared to a typical state-of-the-art governor that employs simple linear regression models.

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MEMCOP: memory-aware co-operative power management governor for mobile games

Cited by 4 publications

References 16 publications

Reflecting on Self-Aware Systems-on-Chip

Reflecting on Self-Aware Systems-on-Chip

Memory-Aware DVFS Governing Policy for Improved Energy-Saving in the Linux Kernel

An Interpretable Machine Learning Model Enhanced Integrated CPU-GPU DVFS Governor

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