Traditional Dynamic Voltage Scaling Research Articles

Discrete feedback-based dynamic voltage scaling for safety critical real-time systems

Recently, the tradeoff between low energy consumption and high fault-tolerance has attracted a lot of attention as a key issue in the design of real-time systems. Dynamic Voltage Scaling (DVS) is commonly employed as one of the most effective low energy techniques for real-time systems. It has been observed that the use of feedback-based methods can improve the effectiveness of DVS-enabled systems. In this paper, we have investigated reducing the energy consumption of fault-tolerant hard real-time systems using the feedback control theory. Our proposed method makes the system capable of selecting the proper frequency and voltage settings in order to reduce the energy consumption, while guaranteeing hard real-time requirements in the presence of unpredictable workload fluctuations and faults. In the proposed method, the available slack-time is exploited by a feedback-based DVS at runtime to reduce the energy consumption. Furthermore, some slack-time is reserved for re-execution in case of faults. The simulation results show that compared with the traditional DVS method, our proposed method not only provides up to 59% energy saving, but also satisfies hard real-time constraints. Our proposed method is also effective in harnessing the static energy. The transition overheads are also taken into account in our simulation experiments.

RazorProtector: Maintaining Razor DVS Efficiency in Large IR-Drop Zones by an Adaptive Redundant Data-Path

Recently, the DVS (Dynamic Voltage Scaling) method has been aggressively applied to processors with Razor Flip-Flops. With Razor FF detecting setup errors, the supply voltage in these processors is down-scaled to a near critical setup timing level for a maximum power consumption reduction. However, the conventional Razor and DVS combinations cannot tolerate well error rate variations caused by IR-drops and environment changes. At the near critical setup timing point, even a small error rate change will result in sharp performance degradation. In this paper, we propose RazorProtector, a DVS application method based on a redundant data-path which uses a multi-cycle redundant calculation to shorten the recovery penalty after a setup error occurrence. A dynamic redundancy-adapting scheme is also given to use effectively the designed redundant data-path based on a study of the program, device and error rate characteristics. Our results show that RazorProtector with the adaptive redundancy architecture can, compared to the traditional DVS method with Razor FF, under a large setup rate caused by a 10% unwanted voltage drop, reduce EDP up to 78% at 100µs/V, 88% at 200µs/V voltage scaling slope.

Enhanced Energy-Aware Feedback Scheduling of Embedded Control Systems

Dynamic voltage scaling (DVS) is one of the most effective techniques for reducing energy consumption in embedded and real-time systems. However, traditional DVS algorithms have inherent limitations on their capability in energy saving since they rarely take into account the actual application requirements and often exploit fixed timing constraints of real-time tasks. Taking advantage of application adaptation, an enhanced energy-aware feedback scheduling (EEAFS) scheme is proposed, which integrates feedback scheduling with DVS. To achieve further reduction in energy consumption over pure DVS while not jeopardizing the quality of control, the sampling period of each control loop is adapted to its actual control performance, thus exploring flexible timing constraints on control tasks. Extensive simulation results are given to demonstrate the effectiveness of EEAFS under different scenarios. Compared with the optimal pure DVS scheme, EEAFS saves much more energy while yielding comparable control performance.

A Dynamic Voltage Scaling Algorithm for Dynamic Workloads

Dynamic Voltage Scaling (DVS) is a promising method to achieve energy saving by slowing down the processor into multiple frequency levels in battery-operated embedded systems. However, the worst case execution time (WCET) of the tasks scheduled by DVS must be known ahead of time to ensure their schedulability. In reality, a system's workloads may change significantly without satisfying any prediction. In other words, a task's WCET may not provide useful information about its future real execution time (RET). This paper presents a novel Dynamic-Mode EDF scheduling algorithm when workloads change significantly. One of the Single-Mode, Dual-Mode, and Three-Mode frequency setting formats can be applied, based on the RET and the accumulated slack at run-time. Only one combination of the number of modes/speeds, speed-switching transition points, and the frequency scaling factor for each mode can lead to the best energy saving. Experimental results show that, given an RET pattern, our Dynamic-Mode DVS algorithm achieves an average 15% energy savings over the traditional two-mode DVS scheme on hard real-time systems. Additionally, we also consider speed-switching or energy transition overhead, and implement a preliminary test of our proposed algorithm. With a less aggressive voltage scaling strategy (fewer speed changes for each job), deadlines can still be strictly satisfied and an average of 14% energy consumption saving over a non-DVS scheme is observed.

A Self-Tuning DVS Processor Using Delay-Error Detection and Correction

In this paper, we present a dynamic voltage scaling (DVS) technique called Razor which incorporates an in situ error detection and correction mechanism to recover from timing errors. We also present the implementation details and silicon measurements results of a 64-bit processor fabricated in 0.18-/spl mu/m technology that uses Razor for supply voltage control. Traditional DVS techniques require significant voltage safety margins to guarantee computational correctness at the worst case combination of process, voltage and temperature conditions, leading to a loss in energy efficiency. In Razor-based DVS, however, the supply voltage is automatically reduced to the point of first failure using the error detection and correction mechanism, thereby eliminating safety margins while still ensuring correct operation. In addition, the supply voltage can be intentionally scaled below the point of first failure of the processor to achieve an optimal tradeoff between energy savings from further voltage reduction and energy overhead from increased error detection and correction activity. We tested and measured savings due to Razor DVS for 33 different dies and obtained an average energy savings of 50% over worst case operating conditions by scaling supply voltage to achieve a 0.1% targeted error rate, at a fixed frequency of 120 MHz.

A dynamic-mode DVS algorithm under dynamic workloads

Dynamic Voltage Scaling (DVS) is a promising method to achieve energy saving by slowing down the processor into multiple frequency levels especially in battery-operated embedded systems. We introduce a novel Dynamic-Mode EDF scheduling algorithm when workloads change significantly. Single-Mode, Dual-Mode and Three-Mode frequency setting formats can be applied, based on the Real Execution Time (RET) and the slack time at run-time. Only one combination of the time zone length and the frequency scaling factor can lead to the best energy saving. Experimental results show that, given an RET pattern, our Dynamic-Mode DVS algorithm achieves an average 15% energy savings over the traditional two-mode DVS scheme on hard real-time systems.

Managing power consumption in networks on chips

In this paper, we present a new methodology for managing power consumption of networks-on-chips (NOCs). A power management problem is formulated for the first time using closed-loop control concepts. We introduce an estimator and a controller that implement our power management methodology. The estimator is capable of very fast and accurate tracking of changes in the system parameters. Parameters estimated are used to form the system model. Our system model combines node and network centric power management decisions. Node centric power management assumes no a priori knowledge of requests coming in from outside the core. Thus, it implements a more traditional dynamic voltage scaling and power management control algorithms. Network-centric power management utilizes interaction with the other system cores regarding the power and the quality of service (QoS) needs. The overall system model is based on Renewal theory and, thus, guarantees globally optimal results. We introduce a fast optimization method that runs multiple orders of magnitude faster than the previous optimization approaches while still having the same accuracy in obtaining the power management control. Finally, our controller implements the results of optimization in either hardware or software. The new methodology for power management of NOCs is tested on a system consisting of four satellite units, each implementing an estimator and a controller capable of both node and network centric power management. Our results show large savings in power with good QoS.