Exploiting Heterogeneity for Aging-Aware Load Balancing in Mobile Platforms

Abstract

The pervasiveness of heterogeneous multiprocessors (HMP) in the mobile domain enables more energy efficient systems. However, current approaches to exploit the energy efficiency of HMPs results in unbalanced usage of resources, which leads to higher aging rates and delay degradation when compared to homogeneous architectures. In this paper, we propose ADAMANT, an aging-aware task mapping algorithm for HMPs. ADAMANT exploits on-chip sensing of aging, performance, and power in orderto enable on-line workload characterization to select task-to-core mappings that yield both increased system lifetime and energy efficiency. Experimental evaluation using a typical mobile workload demonstrates an improvement in chip lifetime by up to 2x on a big.LITTLE architecture.