Run-time resource management of heterogeneous multi-core systems is challenging due to i) dynamic workloads, that often result in ii) conflicting knob actuation decisions, which potentially iii) compromise on performance for thermal safety. We present a runtime resource management strategy for performance guarantees under power constraints using functionally approximate kernels that exploit accuracy-performance trade-offs within error resilient applications. Our controller integrates approximation with power knobs-DVFS, CPU quota, task migration-in coordinated manner to make performance-aware decisions on power management under variable workloads. Experimental results on Odroid XU3 show the effectiveness of this strategy in meeting performance requirements without power violations compared to existing solutions.

Approximation-aware coordinated power/performance management for heterogeneous multi-cores

Miele, Antonio;Bolchini, Cristiana;
2018-01-01

Abstract

Run-time resource management of heterogeneous multi-core systems is challenging due to i) dynamic workloads, that often result in ii) conflicting knob actuation decisions, which potentially iii) compromise on performance for thermal safety. We present a runtime resource management strategy for performance guarantees under power constraints using functionally approximate kernels that exploit accuracy-performance trade-offs within error resilient applications. Our controller integrates approximation with power knobs-DVFS, CPU quota, task migration-in coordinated manner to make performance-aware decisions on power management under variable workloads. Experimental results on Odroid XU3 show the effectiveness of this strategy in meeting performance requirements without power violations compared to existing solutions.
2018
Proc. Design Automation Conference
9781450357005
Approximate computing; On-chip resource management; Computer Science Applications1707 Computer Vision and Pattern Recognition; Control and Systems Engineering; Electrical and Electronic Engineering; Modeling and Simulation
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/1076051
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