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.
Highlights
As smartphones have evolved and become more widespread, users have spent increasing amounts of time using these devices
This paper proposes a method based on Long Short-Term Memory (LSTM) to track and predict processor usage more accurately than the existing methods
Studies[8]–[10] were conducted to optimize energy efficiency of mobile processors by finding configurations that maximize performance per watt (PPW) or instructions per second per watt (IPS/W) in a given state. These methods inevitably result in performance degradation of application performance since higher frequency requires more supply voltage and current, and the processor power consumption is proportional to the square of the current
Summary
As smartphones have evolved and become more widespread, users have spent increasing amounts of time using these devices. Studies[8]–[10] were conducted to optimize energy efficiency of mobile processors by finding configurations that maximize performance per watt (PPW) or instructions per second per watt (IPS/W) in a given state These methods inevitably result in performance degradation of application performance since higher frequency requires more supply voltage and current, and the processor power consumption is proportional to the square of the current. The proposed method in this paper reduces power consumption effectively while preventing degradation in application performance For this purpose, an LSTM-based processor usage prediction model and a neuro-governor architecture are proposed. If the LSTM network has not been trained yet, Check CPU operates in interactive governor mode
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