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Dynamic resource management has become one of the major areas of research inmodern computer and communication system design due to lower power consumptionand higher performance demands. The number of integrated cores, level ofheterogeneity and amount of control knobs increase steadily. As a result, thesystem complexity is increasing faster than our ability to optimize anddynamically manage the resources. Moreover, offline approaches are sub-optimaldue to workload variations and large volume of new applications unknown atdesign time. This paper first reviews recent online learning techniques forpredicting system performance, power, and temperature. Then, we describe theuse of predictive models for online control using two modern approaches:imitation learning (IL) and an explicit nonlinear model predictive control(NMPC). Evaluations on a commercial mobile platform with 16 benchmarks showthat the IL approach successfully adapts the control policy to unknownapplications. The explicit NMPC provides 25% energy savings compared to astate-of-the-art algorithm for multi-variable power management of modern GPUsub-systems.

Online Adaptive Learning for Runtime Resource Management of Heterogeneous SoCs