Learning transfer-based adaptive energy minimization in embedded systems

Embedded systems execute applications with different performance requirements. These applications exercise the hardware differently depending on the types of computation being carried out, generating varying workloads with time. We will demonstrate that energy minimization with such workload and performance variations within (intra) and across (inter) applications is particularly challenging. To address this challenge we propose an online energy minimization approach, capable of minimizing energy through adaptation to these variations. At the core of the approach is an initial learning through reinforcement learning algorithm that suitably selects the appropriate voltage/frequency scalings (VFS) based on workload predictions to meet the applications’ performance requirements. The adaptation is then facilitated and expedited through learning transfer, which uses the interaction between the system application, runtime and hardware layers to adjust the power control levers. The proposed approach is implemented as a power governor in Linux and validated on an ARM Cortex-A8 running different benchmark applications. We show that with intra- and inter-application variations, our proposed approach can effectively minimize energy consumption by up to 33% compared to existing approaches. Scaling the approach further to multi-core systems, we also show that it can minimize energy by up to 18% with 2X reduction in the learning time when compared with a recently reported approach