Learning Pareto-Frontier Resource Management Policies for Heterogeneous SoCs: An Information-Theoretic Approach

TL;DR

This paper introduces PaRMIS, an information-theoretic framework that learns Pareto-optimal resource management policies for heterogeneous mobile SoCs, effectively balancing multiple objectives like performance and energy consumption.

Contribution

It presents a novel information-theoretic approach to generate Pareto-optimal policies for complex SoC resource management problems, outperforming prior methods.

Findings

PaRMIS achieves better Pareto fronts than previous methods.

It effectively optimizes complex objectives such as performance per Watt.

The framework is easily applicable to real-world heterogeneous SoC scenarios.

Abstract

Mobile system-on-chips (SoCs) are growing in their complexity and heterogeneity (e.g., Arm's Big-Little architecture) to meet the needs of emerging applications, including games and artificial intelligence. This makes it very challenging to optimally manage the resources (e.g., controlling the number and frequency of different types of cores) at runtime to meet the desired trade-offs among multiple objectives such as performance and energy. This paper proposes a novel information-theoretic framework referred to as PaRMIS to create Pareto-optimal resource management policies for given target applications and design objectives. PaRMIS specifies parametric policies to manage resources and learns statistical models from candidate policy evaluation data in the form of target design objective values. The key idea is to select a candidate policy for evaluation in each iteration guided by statistical models that maximize the information gain about the true Pareto front. Experiments on a commercial heterogeneous SoC show that PaRMIS achieves better Pareto fronts and is easily usable to optimize complex objectives (e.g., performance per Watt) when compared to prior methods.