Exploiting Approximation for Run-time Resource Management of Embedded HMPs

: Taufique, Zain; Kanduri, Anil; Miele, Antonio; Rahmani, Amir; Bolchini, Cristiana; Dutt, Nikil; Liljeberg, Pasi

Publisher: Association for Computing Machinery (ACM)

: 2025

: ACM Transactions in Embedded Computing Systems

: ACM Transactions on Embedded Computing Systems

: 39

: 24

: 3

: 1539-9087

: 1558-3465

DOI: https://doi.org/10.1145/3723357(external)

: https://doi.org/10.1145/3723357(external)

: https://research.utu.fi/converis/portal/detail/Publication/498468564(external)

Run-time resource management (RTM) of multi-programmed workloads on heterogeneous multi-core platforms is challenging due to (i) fixed power budget of the device, (ii) variable performance requirements of the workloads, and (iii) unknown arrival of the applications. Existing RTM solutions lack power-performance coordination, resulting in performance degradation during power actuation or power violations during performance provisioning. Exploiting inherent error-resilience of the applications can address the performance loss incurred in power actuation, by combining run-time approximation with traditional power knobs (including Dynamic Voltage/Frequency Scaling, Task Migration, Degree of Parallelism, and CPU Quota). In this work, we present an accuracy-aware resource management framework that jointly actuates run-time approximation and traditional power knobs for efficient power-performance management of multi-programmed and multi-threaded workloads running on heterogeneous mobile platforms. Our strategy configures the accuracy of the applications at run-time to exploit accuracy-performance trade-offs, by considering system-wide power-performance dynamics. We use heuristic estimation models to jointly enforce accuracy configuration and traditional power knobs settings at run-time. We evaluated our framework on real-world embedded mobile platforms, including Odroid XU3 and Asus Tinker Edge R boards to demonstrate the efficiency of our proposed approach across multiple workload scenarios. Our approach achieved 25% lower performance violations against the state-of-the-art run-time resource management policies at the cost of 2.2% accuracy loss across six applications.

3723357.pdf

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This work is supported by the European Union’s Horizon 2020 Research and Innovation Programme under the Marie Skłodowska Curie Grant No. 956090 (APROPOS).