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Adaptive and Hierarchical Runtime Manager for Energy-Aware Thermal Management of Embedded Systems

Published:29 January 2016Publication History
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Abstract

Modern embedded systems execute applications, which interact with the operating system and hardware differently depending on the type of workload. These cross-layer interactions result in wide variations of the chip-wide thermal profile. In this article, a reinforcement learning-based runtime manager is proposed that guarantees application-specific performance requirements and controls the POSIX thread allocation and voltage/frequency scaling for energy-efficient thermal management. This controls three thermal aspects: peak temperature, average temperature, and thermal cycling. Contrary to existing learning-based runtime approaches that optimize energy and temperature individually, the proposed runtime manager is the first approach to combine the two objectives, simultaneously addressing all three thermal aspects. However, determining thread allocation and core frequencies to optimize energy and temperature is an NP-hard problem. This leads to exponential growth in the learning table (significant memory overhead) and a corresponding increase in the exploration time to learn the most appropriate thread allocation and core frequency for a particular application workload. To confine the learning space and to minimize the learning cost, the proposed runtime manager is implemented in a two-stage hierarchy: a heuristic-based thread allocation at a longer time interval to improve thermal cycling, followed by a learning-based hardware frequency selection at a much finer interval to improve average temperature, peak temperature, and energy consumption. This enables finer control on temperature in an energy-efficient manner while simultaneously addressing scalability, which is a crucial aspect for multi-/many-core embedded systems. The proposed hierarchical runtime manager is implemented for Linux running on nVidia’s Tegra SoC, featuring four ARM Cortex-A15 cores. Experiments conducted with a range of embedded and cpu-intensive applications demonstrate that the proposed runtime manager not only reduces energy consumption by an average 15% with respect to Linux but also improves all the thermal aspects—average temperature by 14°C, peak temperature by 16°C, and thermal cycling by 54%.

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      • Published in

        cover image ACM Transactions on Embedded Computing Systems
        ACM Transactions on Embedded Computing Systems  Volume 15, Issue 2
        Special Issue on Innovative Design, Special Issue on MEMOCODE 2014 and Special Issue on M2M/IOT
        May 2016
        421 pages
        ISSN:1539-9087
        EISSN:1558-3465
        DOI:10.1145/2888407
        Issue’s Table of Contents

        Copyright © 2016 ACM

        Publisher

        Association for Computing Machinery

        New York, NY, United States

        Publication History

        • Published: 29 January 2016
        • Accepted: 1 September 2015
        • Revised: 1 June 2015
        • Received: 1 December 2014
        Published in tecs Volume 15, Issue 2

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