Introduction An electronic system can be viewed as a collection of different types of components. Some components have fixed performance specifications and energy consumption; these are called non-power-managed components. Conversely, some components can operate at different times and have multiple energy consumption states, consuming different amounts of system power accordingly; these are called power-managed components. The effective use of power-managed components is key to saving system energy consumption and enabling the entire system to operate for extended periods with limited power. It often takes time for system components to transition from one energy consumption state to another, and during this time, additional energy is consumed. State changes affect system performance, so designers need to find an appropriate trade-off between energy saving and system performance. This article introduces some methods in dynamic power management. These methods will determine whether and when components change their energy consumption states. [b]1 Dynamic Power Management Technology[/b] Dynamic power management is a design method that dynamically allocates system resources to complete system tasks with the fewest components or the lowest possible workload, thus reducing power consumption. Determining when to implement power management requires various prediction methods, using historical workloads to predict upcoming workloads and decide whether and when to switch operating states. This is the core of dynamic power management technology—the dynamic power management method itself. A fundamental premise for the applicability of dynamic power management technology is that system components have varying workloads during their operating time. Most systems exhibit this characteristic. Another premise is the ability to predict the fluctuations in the workload of the system and its components to a certain extent. This allows for the possibility of switching energy consumption states, and ensures that the system does not consume excessive energy within the timeframe of workload observation and prediction. **2 Power Management** When a system device receives a request, it becomes busy; when there is no request, it enters an idle state. When idle, the device can be shut down and enter a low-power sleep state; when a request is received again, the device is awakened. This is called "power management." However, changing the power consumption state takes time, namely shutdown latency and wake-up latency. Waking up a device in a sleep state requires additional energy expenditure, as shown in Figure 1. Without this expenditure, power management technology would be unnecessary; the device could simply be shut down whenever it is idle. Since this latency and energy expenditure are certain, it must be considered. A device should only enter a sleep state if the energy saved during sleep is at least offsetting the energy expenditure of the state transition. Power management is a predictive problem. It is necessary to determine whether the idle time is long enough to offset the energy expenditure of the state transition. If the idle time is too short, the power management solution becomes counterproductive. Therefore, estimating the length of the idle time in advance is the primary issue in power management technology. Define the "appropriate pause period" (tBE): the shortest idle time period that achieves system energy saving. This time is related to the device components themselves and is independent of the requests issued by the system. Assuming the energy consumption of the state transition delay t0 (including shutdown and wake-up delays) is E0; the operating power is Pw, and the sleep power is Ps, tBE can be calculated using the following formula: Pw × tBE = E0 + Ps × (tBE - T0). The left side of the equation represents the energy consumption within the "appropriate pause period," which is the energy required for the system to continue operating during this shortest idle time for energy saving; the right side represents the energy consumption during state transitions and the system energy consumption during the sleep period. tBE is the sum of the energy consumption during the sleep period and the system energy consumption during this sleep period. Power management technology aims to predict whether the upcoming sleep time will exceed tBE; only if it does so is the device necessary to sleep. [b]3 Dynamic Power Management Techniques Based on Prior Knowledge[/b] For most real-world systems, the incoming signal is difficult to determine. Dynamic power management decisions are based on uncertain predictions of the future. The fundamental principle of all prediction-based dynamic power management techniques is to explore the relationship between past workload history and upcoming workload to reliably predict future events. For dynamic power management, we are concerned with predicting a sufficiently long idle time to enter a sleep state, expressed as follows: p = {tIDLE>tBE} We call a predicted idle time longer (shorter) than the actual idle time "over-predicted" ("under-predicted"). Over-prediction increases the impact on performance; under-prediction, while having no impact on performance, wastes energy. Ideal prediction is one that is neither over-predicted nor under-predicted. The quality of prediction depends on the selection of the observation sample and the statistics of the workload. 3.1 Static Prediction Methods Fixed Timeout Method: The most common power management prediction method, using past idle time as the observation basis to predict the total duration of the current idle period. This method can be summarized as follows: The idle clock starts, the timer begins counting, and if the system remains idle after a fixed timeout period tTO, power management puts the system into hibernation until an external request is received, marking the end of the idle state. Choosing the appropriate tTO is crucial to this method. Typically, tTO = tBE is chosen when requirements are not high. The fixed timeout method has two advantages: ① It is universally applicable (its application depends only on workload); ② Increasing the fixed timeout value can reduce the possibility of "over-prediction" (i.e., the predicted time is longer than the actual idle time). However, its disadvantages are also obvious: an excessively large fixed timeout will lead to under-prediction, resulting in ineffective energy saving and a significant amount of energy wasted waiting for the timeout. Predictive shutdown method: This method solves the problem of excessive energy consumption due to waiting for the fixed timeout in the fixed timeout method. It shuts down the system immediately upon anticipating the possibility of system idleness, without waiting for the idle time to exceed the timeout value. The predictive method is a positive estimate based on historical workload statistics. Srivastave proposed two a priori shutdown schemes: ① Nonlinear decay equation (φ). This equation can be obtained from past history. The superscript of 't' indicates the sequence number of past idle and working periods, and 'n' represents the current idle period (whose length needs to be estimated) and the most recent working period. This equation shows that to estimate the upcoming idle period, past idle and working periods must be taken into account. If tpred > tBE, then the system shuts down immediately upon becoming idle. The limitations of this method are: * It cannot autonomously determine the type of decay equation; * A decay model must be built based on collected and analyzed dispersed data, and this data must fit the decay model. ② Limiting approach. This approach is based on a limit. The observed sample is the working period immediately preceding the current idle period. If the idle period is considered longer than the previous working period, then the system shuts down. Note: Statistical studies show that short working periods are followed by long idle periods, and long working periods are followed by short idle periods. Such systems can use the limiting method, as shown in Figure 2. However, the limiting method cannot be used in cases where short working periods are followed by short idle periods. In short, the choice of tthr is particularly important. Predictive wake-up method: This method can solve the performance loss during wake-up in the fixed timeout method. The system wakes up when the predicted idle time expires, even if no system request has been received at that time. It should be noted that if the timeout is "insufficiently predicted," this method increases energy consumption, but it also reduces the time spent waiting to receive the first system request, thus saving energy and improving system performance to some extent. 3.2 Dynamic predictive method: Since the optimization of dynamic power management methods depends on the statistics of workload, static predictive methods are not very effective when the workload is neither known nor static. Therefore, dynamic predictive methods exist. There are several dynamic predictive methods for non-static workloads. ① Set a set of timeout values, each associated with a parameter. This parameter indicates the accuracy of the timeout value selection. This method selects the most effective timeout value within each idle time. ② This method also has several timeout values ​​to choose from, assigning a "weight" to each value. This "weight" is an abstract parameter based on the satisfaction gained from using this timeout value under the same requirements in the past. The actual timeout value used is the weighted average of all selected timeout values. ③ Only one timeout value is used. If selecting this timeout value causes many unsatisfactory "system shutdowns," then this value is appropriately increased. When more "system shutdowns" become acceptable, this value is appropriately decreased. **4. Conclusion** Dynamic power management is an effective design method for reducing the energy consumption of electronic systems. In a power management system, the operating states of different components must dynamically adapt to different performance requirements. Only in this way can the energy wasted during idle time or by unused components be minimized. Designing a power management scheme that achieves minimum energy consumption under certain performance constraints is the most challenging aspect. The most basic power management method can be referenced in the scheme of prior knowledge, which explores the relationship between the history of past workloads and upcoming workloads, thereby reliably predicting future events. The application of dynamic power management is still rudimentary because its full potential has not yet been explored; the complexity of the different types of components involved limits designers to seeking only simple solutions. We will continue to study the corresponding algorithms in order to provide a correct model for dynamic power management and determine the optimal power management scheme for the system under the model. (Edited by: He Shiping)