Memory-aware dynamic voltage scaling for multimedia applications

With the scaling of technology and higher requirements on performance and functionality, power dissipation is becoming one of the major design considerations in the development of network processors. In this paper, we use an assertion-based methodology for system-level power/performance analysis to study two dynamic voltage scaling (DVS) techniques, traffic-based DVS and execution-based DVS, in a network processor model. Using the automatically generated distribution analyzers, we analyze the power and performance distributions and study their trade-offs for the two DVS policies with different parameter settings such as threshold values and window sizes. We discuss the optimal configurations of the two DVS policies under different design requirements. By a set of experiments, we show that the assertion-based trace analysis methodology is an efficient tool that can help a designer easily compare and study optimal architectural configurations in a large design space.

Negative Capacitance Field-Effect Transistor (NCFET) has recently attracted significant attention. In the NCFET technology with a thick ferroelectric layer, voltage reduction increases the leakage power, rather than decreases, due to the negative Drain-Induced Barrier Lowering (DIBL) effect. This work is the first to demonstrate the far-reaching consequences of such an inverse dependency w.r.t. the existing power management techniques. Moreover, this work is the first to demonstrate that state-of-the-art Dynamic Voltage Scaling (DVS) techniques are sub-optimal for NCFET. Our investigation revealed that the optimal voltage at which the total power is minimized is not necessarily at the point of the minimum voltage required to fulfill the performance constraint (as in traditional DVS). Hence, an NCFET-aware DVS is key for high energy efficiency. In this work, we therefore propose the first NCFET-aware DVS technique that selects the optimal voltage to minimize the power following the dynamics of workloads. Our experimental results of a multi-core system demonstrate that NCFET-aware DVS results in 20% on average, and up to 27% energy saving while still fulfilling the same performance constraint (i.e., no trade-offs) compared to traditional NCFET-unaware DVS techniques.

This paper describes a new dynamic voltage scaling (DVS) technique for embedded systems expressed as conditional task graphs (CTGs). The idea is to identify and exploit the available worst case slack time, taking into account the conditional behaviour of CTGs. Also we examine the effect of combining a genetic algorithm based mapping with the DVS technique for CTGs and show that further energy reduction can be obtained. The techniques have been tested on a number of CTGs including a real-life example. The results show that the DVS technique can be applied to CTGs with energy saving up to 24%. Furthermore it is shown that savings of up to 51% are achieved by considering DVS during the mapping.

Compiler-directed dynamic voltage scaling (DVS) is one of the effective low-power techniques for real-time applications. Using the technique, compiler inserts voltage scaling points into a real-time application, and supply voltage and clock frequency are adjusted to the relationship between the remaining time and the remaining workload at each voltage scaling point. In this paper, based on the WCET (the worst case execution time) analysis tool HEPTANE and the performance/power simulator Sim-Panalyzer, we present a DVS-enabled simulation environment RTLPower (Real-Time Low Power), which integrates static WCET estimation, performance/power simulation, automatically inserting the DVS code into a real-time application, and profile-guided energy optimization. By simulations of some benchmark applications, we prove that the DVS technique and the profile-guided optimization technique significantly reduce energy consumption.

Significant power savings can be achieved on voltage/ frequency configurable platforms by dynamically adapting the frequency and voltage according to the workload (complexity). Video decoding is one of the most complex tasks performed on such systems due to its computationally demanding operations like inverse filtering, interpolation, motion compensation and entropy decoding. Dynamically adapting the frequency and voltage for video decoding is attractive due to the time-varying workload and because the utility of decoding a frame is dependent only on decoding the frame before the display deadline. Our contribution in this paper is twofold. First, we adopt a complexity model that explicitly considers the video compression and platform specifics to accurately predict execution times. Second, based on this complexity model, we propose a dynamic voltage scaling algorithm that changes effective deadlines of frame decoding jobs. We pose our problem as a buffer-constrained optimization and show that significant improvements can be achieved over the state-of-the-art dynamic voltage scaling techniques without any performance degradation. Index

For real time applications, time slacks of a preliminary task schedule may be exploited to conserve energy. This can be accomplished by leveraging the dynamic voltage scaling (DVS) technique to slow down clock frequency of certain cores as long as the deadline is met. In this chapter, the task of fine-tuning an existing task assignment and schedule and using DVS to lower the overall energy consumption is formulated as a graph-theoretic maximum weight clique (MWC) problem. An efficient heuristic algorithm is proposed to systematically solve this problem. A unique feature of our approach is concurrently applying DVS to slow down the execution of multiple tasks to achieve better energy savings. Extensive simulations are performed to compare this proposed algorithm against leading energy-aware task scheduling algorithm and DVS algorithm. Our algorithm exhibits 22% more energy savings than the Energy Aware Scheduling (EAS) algorithm. As for energy saving in DVS process, our MWC-based method provides a 97% saving improvement over the PV-DVS algorithm.

Many computing systems have adopted the dynamic voltage scaling (DVS) technique to reduce energy consumption by slowing down operation speed. However, The longer a job executes, the more energy in leakage current the processor consumes for the job. To reduce the power/energy consumption from the leakage current, a processor can enter the dormant mode. Existing research results for leakage-aware DVS scheduling perform procrastination of real-time jobs greedily so that the idle time can be aggregated as long as possible to turn off the processor. This paper proposes algorithms for the procrastination determination of periodic real-time tasks in uniprocessor systems. Instead of greedy procrastination, the procrastination procedures are applied only when the evaluated energy consumption is less than not procrastination. Evaluation results show that our proposed algorithms could derive energy-efficient solutions and outperform existing algorithms. Keywords: Energy-aware systems. .Scheduling, Leakage-aware scheduling. Dynamic voltage scaling. Job procrastination.

Dynamic voltage scaling (DVS) is an effective method for reducing processor power consumption. We present a compiler-based technique for DVS-based power optimizations of multimedia applications in the context of the Multi-Level Computing Architecture (MLCA) a novel architecture for parallel systems-on-a-chip. Our technique combines dependence analysis of long-running loops with profiling information in order to identify the slack available in the execution of parallel tasks. DVS is then applied to slow down processors executing noncritical-path tasks, reducing power with little or no impact on execution time. We evaluate our technique using realistic multimedia applications and a simulator of the MLCA. The results demonstrate that up to 10% savings in processor power consumption can be achieved with no more than 1.5% increase in execution time. Although our technique is developed in the context of MLCA, we believe that it is applicable in the broader context of task-level parallelism in multimedia applications.

Energy consumption has become a major constraint in providing increased functionality for devices with small form factors. Dynamic voltage and frequency scaling has been identified as an effective approach for reducing the energy consumption of embedded systems. Earlier works on dynamic voltage scaling focused mainly on performing voltage scaling when the CPU is waiting for memory subsystem or concentrated chiefly on loop nests and/or subroutine calls having sufficient number of dynamic instructions. This paper concentrates on coarser program regions and for the first time uses program phase behavior for performing dynamic voltage scaling. Program phases are annotated at compile time with mode switch instructions. Further, we relate the Dynamic Voltage Scaling Problem to the Multiple Choice Knapsack Problem, and use well known heuristics to solve it efficiently. Also, we develop a simple integer linear program formulation for this problem. Experimental evaluation on a set of media applications reveal that our heuristic method obtains a 38% reduction in energy consumption on an average, with a performance degradation of 1% and upto 45% reduction in energy with a performance degradation of 5%. Further, the energy consumed by the heuristic solution is within 1% of the optimal solution obtained from the ILP approach.KeywordsExecution TimeSupply VoltageKnapsack ProblemBasic BlockProgram RegionThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

An integrated buck-boost converter with a tri-mode digital control is presented. It employs an adaptive step-up/down voltage conversion to enable a wide range of output voltage. This is beneficial to the ever-increasing electronic systems that employ dynamic voltage scaling (DVS) techniques for power-efficient operations. Three control modes are employed in the converter to operate jointly and seamlessly for performance optimization during the periods of start-up, steady state, and dynamic voltage/load transient states. To avoid latch-up problem and minimize leakage current, an automatic substrate switching circuit (ASSC) is introduced. The design was fabricated in a 0.35-?m digital CMOS N-well process, with a die area of 1.3 mm2. It precisely regulates an adaptively adjustable power output from 0.9 to 3.0 V. The maximum efficiency is 96.5%, which is measured at 0.9-V output and 45-mW load power. The efficiency remains above 50% over the entire 800-mW power range. The converter responds to a 45-mA load step transient change within 600 ns. The DVS tracking speed is 37 ?s/V for a 1-V step-down change and is 150 ?s/V for a 1-V step-up change. With a line regulation of 20.4 mV/V, the converter functions robustly when the input power source frequently varies between 1.6 and 3.3 V. The ASSC consumes only 88- ?W static power.

As the dynamic voltage scaling (DVS) technique provides system engineers the flexibility to trade-off the performance and the energy consumption, DVS has been adopted in many computing systems. However, the longer a job executes, the more energy in the leakage current the device/processor consumes for the job. To reduce the energy consumption resulting from the leakage current, a system might enter the dormant mode. This paper targets energy-efficient rate-monotonic scheduling for periodic real-time tasks on a uniprocessor DVS system with non-negligible leakage power consumption. An on-line simulated scheduling strategy and a virtually blocking time strategy are developed for procrastination scheduling to reduce energy consumption. The proposed algorithms derive a feasible schedule for real-time tasks with worst-case guarantees for any input instance. Experimental results show that our proposed algorithms could derive energy-efficient solutions.

In this paper, we focus on joint energy and performance optimization for streaming applications on multiprocessor systems-on-chip by combining task-level coarse-grained software pipelining with DVS (dynamic voltage scaling)and DPM (dynamic power management) techniques with the considerations of transition overhead, inter-processor communication and discrete voltage levels. We propose a two-phase approach to solve the problem. In the first phase, we propose a coarse-grained task parallelization algorithm called RDAG to transform a periodic dependent task graph into a set of independent tasks based on the retiming technique[19]. In the second phase, we propose a novel scheduling algorithm called SpringS that works like a spring by iteratively adjusting task scheduling and voltage selection by combining DVS and DPM. We conduct experiments with a set of benchmarks from E3S [10] and TGFF [27]. The experimental results show that our technique can achieve 49:8% energy saving on average compared with the approach in [20], that applied DVS and DPM without software pipelining. In addition, given a tight timing constraint, our technique can obtain a feasible solution while the approach in [20] cannot.

We present a dynamic voltage and frequency scaling technique within SoCs for per-core power management: the architecture allows for individual, self triggered performance-level scaling of the processing elements (PEs) within less than 100ns. This technique enables each core to adjust its local supply voltage and frequency depending on its current computational load. A test chip has been implemented in 28nm CMOS technology, as prototype of the SpiNNaker2 neuromorphic many core system, containing 4 PEs which are operational within the range of 1.1V down to 0.7V at frequencies from 666MHz down to 100MHz; the effectiveness of the power management technique is demonstrated using a standard benchmark from the application domain. The particular domain area of this application specific processor is real-time neuromorphics. Using a standard benchmark - the synfire chain - we show that the total power consumption can be reduced by 45%, with 85% baseline power reduction and a 30% reduction of energy per neuron and synapse computation, all while maintaining biological real-time operation.

Low power consumption is a critical issue in many UWB systems. This paper investigates the application of dynamic voltage scaling (DVS) and other low power design techniques to a multiband-OFDM UWB transceiver baseband circuit design in order to reduce average power consumption of the chip. The results show significant power savings over the conventional approach.

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