Energy-Performance Co-Management of Mixed-Sensitivity Workloads on Heterogeneous Multi-core Systems

Shamsa, Elham; Kanduri, Anil; Rahmani, Amir M.; Liljeberg, Pasi

doi:10.1145/3394885.3431516

Cited by 4 publications

(1 citation statement)

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“…Before making control decisions, the controller has to evaluate the effect of the control decisions on the system. State-of-the-art power management techniques can model and analyze CPU and GPU power [27,[31][32][33][34]. In order to analyze the effect of changing various control knobs, it is necessary to build the power models for the respective components.…”

Section: Introductionmentioning

confidence: 99%

Per-Core Power Modeling for Heterogenous SoCs

et al. 2021

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State-of-the-art mobile platforms, such as smartphones and tablets, are powered by heterogeneous system-on-chips (SoCs). These SoCs are composed of many processing elements, including multiple CPU core clusters (e.g., big.LITTLE cores), graphics processing units (GPUs), memory controllers and other on-chip resources. On the one hand, mobile platforms need to provide a swift response time for interactive apps and high throughput for graphics-oriented workloads; on the other hand, the power consumption must be under tight control to prevent high skin temperatures and energy consumption. Therefore, commercial systems feature a range of mechanisms for dynamic power and temperature control. However, these techniques rely on simple indicators, such as core utilization and total power consumption. System architects are typically limited to the total power consumption, since multiple resources share the same power rail. More importantly, most of the power rails are not exposed to the input/output pins. To address this challenge, this paper presents a thorough methodology to model the power consumption of major resources in heterogeneous SoCs. The proposed models utilize a wide range of performance counters to capture the workload dynamics accurately. Experimental validation on a Nexus 6P phone, powered by an octa-core Snapdragon 810 SoC, showed that the proposed models can estimate the power consumption within a 10% error margin.

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