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

Author:

Mandal Sumit K.1,Bhat Ganapati1,Doppa Janardhan Rao2,Pande Partha Pratim2,Ogras Umit Y.1

Affiliation:

1. Arizona State University, AZ, USA

2. Washington State University, WA, USA

Abstract

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, systems-on-chip (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.

Funder

NSF

National Science Foundation

Semiconductor Research Corporation

USA Army Research Office

Publisher

Association for Computing Machinery (ACM)

Subject

Electrical and Electronic Engineering,Computer Graphics and Computer-Aided Design,Computer Science Applications

Cited by 25 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Modeling and Controlling Many-Core HPC Processors: an Alternative to PID and Moving Average Algorithms;ACM Transactions on Autonomous and Adaptive Systems;2024-09-09

2. Preference-Aware Constrained Multi-Objective Bayesian Optimization;Proceedings of the 7th Joint International Conference on Data Science & Management of Data (11th ACM IKDD CODS and 29th COMAD);2024-01-04

3. Improving User Experience via Reinforcement Learning-Based Resource Management on Mobile Devices;Lecture Notes in Computer Science;2024

4. CNN Workloads Characterization and Integrated CPU–GPU DVFS Governors on Embedded Systems;IEEE Embedded Systems Letters;2023-12

5. McCore: A Holistic Management of High-Performance Heterogeneous Multicores;56th Annual IEEE/ACM International Symposium on Microarchitecture;2023-10-28

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3