Dynamic Voltage Frequency Scaling Techniques Research Articles

Energy efficient mixed task handling on real-time embedded systems using FreeRTOS

With the increased use of embedded devices in Industrial electronics, like relays, battery life has gained more and more attention. Modern processors can use Dynamic Voltage Frequency Scaling (DVFS) techniques for energy reduction and temperature control. However, DVFS is not well supported by systems running with Real-Time operating systems like FreeRTOS, which is a widely used real-time operation system (RTOS) in the industry. Furthermore, energy-efficient DVFS techniques for Mixed Tasksets (including periodic and aperiodic tasks) are hardly investigated. This paper extends the classic well-known DVFS technique Cycle Conserving algorithm to handle Mixed Taskset (CCMT algorithm) and implements it on a real-time embedded platform powered by FreeRTOS. We describe our experience implementing CCMT on a real platform with limited DVFS and corresponding scheduler support. Results show that we can successfully apply CCMT to handle aperiodic requests while meeting the deadlines of the periodic tasks and saving energy on the FreeRTOS platform. The algorithm is tested on an ARM Cortex-M7 processor integrated with frequency scaling and power management. Over 5% to 10% energy savings can be achieved for a standard real-time scheduling mechanism without penalty in application throughput.

Minimizing energy consumption with reliability goal on heterogeneous embedded systems

The embedded systems generally require to be low-powered and highly reliable. In order to achieve the low-power design goal, dynamic voltage frequency scaling (DVFS) technique has been widely employed in various embedded application scenarios. However, DVFS reduces execution frequency, which increases transient faults of the processor dramatically. As a result, the reliability of the application will be severely reduced. In this paper, we aim at minimizing energy consumption with reliability goal for parallel application on heterogeneous embedded systems. Since the reliability of the application is the product of the reliability of all the tasks that belong to the application, the reliability goal of the application is transformed into the reliability goal of each task. Considering that some systems may not support DVFS techniques, two methods are proposed to transform the reliability goal of the application into each task for non-DVFS and DVFS, respectively. Based on the reliability goal transformation methods, two energy-efficient scheduling algorithms with the reliability goal are designed. Experiments with real parallel applications demonstrate that the proposed algorithms have significant improvements in energy efficiency compared with the state-of-the-art algorithms.

Profit: <underline>Pr</underline>iority and P<underline>o</underline>wer/Per<underline>f</underline>ormance Opt<underline>i</underline>miza<underline>t</underline>ion for Many-Core Systems

As power density emerges as the main constraint for many-core systems, controlling power consumption under the thermal design power while maximizing the performance becomes increasingly critical. To dynamically save power, dynamic voltage frequency scaling techniques have proved to be effective and are widely available commercially. Meanwhile, systems have certain performance constraints that the applications should satisfy to ensure quality of service. In this paper, we present an online distributed reinforcement learning (OD-RL)-based DVFS control algorithm for many-core system performance improvement under both power and performance constraints. At the finer grain, a per-core RL method is used to learn the optimal control policy of the voltage/frequency (VF) levels in a model-free manner. At the coarser grain, an efficient global power budget reallocation algorithm is used to maximize the overall performance. The experiments show that compared to the state-of-the-art algorithms: 1) OD-RL produces up to 98% less budget overshoot; 2) up to 23% higher energy efficiency; and 3) two orders of magnitude speedup over state-of-the-art techniques for systems with hundreds of cores. Furthermore, priority-aware OD-RL can better satisfy performance constraints than OD-RL with: 1) $17.8\boldsymbol {\times }$ more epochs satisfying the performance constraints; 2) $5.6\boldsymbol {\times }$ better performance gain; and 3) $20.0\boldsymbol {\times }$ better performance-power tradeoffs under similar efficiency and scalability.

Energy minimization for reliability-guaranteed real-time applications using DVFS and checkpointing techniques

Abstract This paper addresses the energy minimization issue when executing real-time applications that have stringent reliability and deadline requirements. To guarantee the satisfaction of the application’s reliability and deadline requirements, checkpointing, Dynamic Voltage Frequency Scaling (DVFS) and backward fault recovery techniques are used. We formally prove that if using backward fault recovery, executing an application with a uniform frequency or neighboring frequencies if the desired frequency is not available, not only consumes the minimal energy but also results in the highest system reliability. Based on this theoretical conclusion, we develop a strategy that utilizes DVFS and checkpointing techniques to execute real-time applications so that not only the applications reliability and deadline requirements are guaranteed, but also the energy consumption for executing the applications is minimized. The developed strategy needs at most one execution frequency change during the execution of an application, hence, the execution overhead caused by frequency switching is small, which makes the strategy particularly useful for processors with a large frequency switching overhead. We empirically compare the developed real-time application execution strategy with recently published work. The experimental results show that, without sacrificing reliability and deadline satisfaction guarantees, the proposed approach can save up to 12% more energy when compared with other approaches.

Analysis of DVFS Techniques for Improving the GPU Energy Efficiency

Dynamic Voltage Frequency Scaling (DVFS) techniques are used to improve energy efficiency of GPUs. Literature survey and thorough analysis of various schemes on DVFS techniques during the last decade are presented in this paper. Detailed analysis of the schemes is included with respect to comparison of various DVFS techniques over the years. To endow with knowledge of various power management techniques that utilize DVFS during the last decade is the main objective of this paper. During the study, we find that DVFS not only work solely but also in coordination with other power optimization techniques like load balancing and task mapping where performance and energy efficiency are affected by varying the platform and benchmark. Thorough analysis of various schemes on DVFS techniques is presented in this paper such that further research in the field of DVFS can be enhanced.

Hybrid power management in real time embedded systems: an interplay of DVFS and DPM techniques

Energy-aware scheduling of real time applications over multiprocessor systems is considered in this paper. Early research reports that while various energy-saving policies, for instance Dynamic Power Management (DPM) and Dynamic Voltage & Frequency scaling (DVFS) policies, perform well individually for a specific set of operating conditions, they often outperform each other under different workload and/or architecture configuration. Thus, no single policy fits perfectly all operating conditions. Instead of designing new policies for specific operating conditions, this paper proposes a generic power/energy management scheme that takes a set of well-known existing (DPM and DVFS) policies, each of which performs well for a set of conditions, and adapts at runtime to the best-performing policy for any given workload. Experiments are performed using state-of the-art DPM and DVFS policies and the results show that our proposed scheme adapts well to the changing workload and always achieves overall energy savings comparable to that of best-performing policy at any point in time.