Energy-Efficient Control of Mobile Processors Based on Long Short-Term Memory

Abstract

Smartphones that are equipped with high-clock frequency and multi-core processors are being commercially released to provide various services. As the number of cores and the clock speed of a mobile processor increases, its power consumption also increases, and several software approaches to reducing power consumption have been studied. Existing techniques estimate processor usage by measuring the processor usage at a previous time. However, these techniques often waste energy because they assign frequencies above the usage required to prevent degraded user responsiveness. Therefore, this paper proposes a machine learning method to predict the usage that the processor currently requires to prevent performance degradation while reducing power consumption. The proposed method is implemented through a processor power management system based on Long Short-Term Memory (LSTM). This system learns processor usage patterns in a variety of situations and predicts the processor usage required for the current situation. The number of computations required by the LSTM-based technique is analyzed according to the number of neurons and layers, and the computational load is then compared to an existing technique. Furthermore, a benchmarking tool that reflects the characteristics of mobile applications is used to test the performance of the proposed system, which is shown to reduce the power consumption of mobile processors by a maximum of 19% compared to the existing Android processor power management system.

Keywords

• Energy management
• dynamic voltage and frequency scaling
• recurrent neural networks
• mobile device