![(a) Plot of P ( T ) for the Sleep state of the StrongARM SA-1100 processor. The three curves refer to three different workload statistics, computed from real-world CPU traces provided by the IPM monitoring package [5]. (b) Comparison of P ( T ) for the two inactive states of the SA-1100 processor. The two curves refer to the same workload.](https://www.researchgate.net/profile/Luca-Benini/publication/220525732/figure/fig5/AS:325717125550084@1454668422279/a-Plot-of-P-T-for-the-Sleep-state-of-the-StrongARM-SA-1100-processor-The-three_Q320.jpg)
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A survey of design techniques for system-level dynamic power management. IEEE Trans Very Large Scale Integr (VLSI) Syst
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Dynamic power management (DPM) is a design methodology for dynamically reconfiguring systems to provide the requested services and performance levels with a minimum number of active components or a minimum load on such components. DPM encompasses a set of techniques that achieves energy-efficient computation by selectively turning off (or reducing the performance of) system components when they are idle (or partially unexploited). In this paper, we survey several approaches to system-level dynamic power management. We first describe how systems employ power-manageable components and how the use of dynamic reconfiguration can impact the overall power consumption. We then analyze DPM implementation issues in electronic systems, and we survey recent initiatives in standardizing the hardware/software interface to enable software-controlled power management of hardware components.
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... These aspects include communication energy consumption and Non communication energy consumption. Design and manufacturing of less energy consume components of mobile nodes such processors, memory and OS power management strategies [4,5,6] used to reduce non communication energy consumption. During communication energy is consumed in either inactive state of communication or active communication states. ...
MANETs are infrastructure less and can be set up anytime anywhere. One of the main design constraints in mobile Ad Hoc networks (MANETs) is that they are power constrained. Hence, every effort is to be channeled towards reducing power. More precisely, network lifetime is a key design metric in MANETs. In this kind of network the mobility rate of nodes is high, so excessive use of energy and also switching off of nodes will cut links between nodes. In order to maximize the lifetime of ad hoc networks traffic should be sent via a route that can avoid nodes with low energy while minimizing the total transmission power. In a MANET, the energy depletion of a node does not affect the node itself only but the overall network lifetime. In this paper, the focus is on a technique for minimizing energy conservation within the routing protocols of the ad hoc network. A Modified Energy Saving Dynamic Source Routing in MANETs (MESDSR) has been proposed which will efficiently utilize the battery power of the mobile nodes in such a way that the network will get more life time. The simulation was carried out using the NS-2 network simulator.
The energy efficiency of IT estates face increasing scrutiny in terms of consumption and \(CO_{2}\) emissions. Existing research efforts predominantly consider energy efficiency at the server and datacentre level. Meanwhile, the energy aware operation of multi-function printer devices has received relatively little attention. Printing incurs significant energy consumption and cost for organisations, and is estimated to be responsible for 10–16% of ICT-related energy consumption within higher education. We present early efforts to develop energy- and occupany-aware management policies for printers. We evaluate our work against an institutional trace of print jobs and building occupancy. We demonstrate the potential to optimise operation by minimising operating time by 4.8%, reducing the number of on-off state transitions by 35%.
Scheduling n independent tasks onto m identical processors in order to minimize the makespan has been widely studied. As an alternative to classic heuristics, the \(\textsc {Slack}\) algorithm groups tasks by packs of m tasks of similar execution times, and schedules first the packs with the largest differences. It turns out to be very performant in practice, but only few studies have been conducted on its theoretical properties. We derive novel analytical results for \(\textsc {Slack}\), and in particular, we study the performance of this algorithm from an asymptotical point of view, under the assumption that the execution time of the tasks follow a given probability distribution. The study is building on a comparison of the most heavily loaded machine compared to the least loaded one. Furthermore, we extend the results when the objective is to minimize the energy consumption rather than the makespan, since reducing the energy consumption of the computing centers is an ever-growing concern for economical and ecological reasons. Finally, we perform extensive simulations to empirically assess the performance of the algorithms with both synthetic and realistic execution time distributions.
Comprehensive neural network applications led to the customization of a scheme to accelerate the computation on ASIC implementation. Hence, the determination of activation function in a neural network is an indispensable requisite. However, the specific design architecture of an activation function in a digital network encounters several difficulties as these activation functions demand additional hardware resources due to their non-linearity. This paper proposed an efficient hyperbolic tangent (tanh) function, wholly based on stochastic Computing methodology. The Hyperbolic tangent implementation is backed by the clock gating technique to curtail the dynamic power dissipation. The results are derived by implementing two different clock gating techniques on the proposed hardware. In this work, the proposed clock gating-based stochastic design for the implementation of activation function is efficient in terms of performance parameters such as area, power, and delay with negligible accuracy loss. MNIST dataset has been used for checking accuracy on LeNeT benchmark architecture. Furthermore, post-synthesis results show that the proposed clock gating design area is reduced by \(\approx \) 70.62\(\%\), power is reduced by \(\approx \) 58.19\(\%\), and delay is reduced by \(\approx \) 98.87\(\%\) compared to the state of the art.
Nowadays, in the world of high-performance computing, saving energy when great computing power is not needed is a must-to-have feature. This usually involves the implementation of Power Management Systems (PMS) to apply power saving polices such as frequency scaling. In particular, for this feature, the actuators of PMS are usually implemented with Phase- or Frequency-Locked Loops, which should occupy a small area and exhibit a low-power consumption. Additionally, they should be able to generate a wide range of frequencies in the order of a few GHz with a fine granularity of a few hundreds of MHz. Since the core of such loops is a tunable oscillator, in this work we present a pseudo-differential Ring Digitally Controlled Oscillator (DCO) implemented with a standard 28 nm CMOS technology to be used in PMS. The proposed DCO features a well-balanced behavior between the noise performance and a wide tuning range, a low-area, and a low-power consumption.
In the single rent-to-buy decision problem, without a priori knowledge of the amount of time a resource will be used we need to decide when to buy the resource, given that we can rent the resource for $1 per unit time or buy it once and for all for $c . In this paper we study algorithms that make a sequence of single rent-to-buy decisions, using the assumption that the resource use times are independently drawn from an unknown probability distribution. Our study of this rent-to-buy problem is motivated by important systems applications, specifically, problems arising from deciding when to spindown disks to conserve energy in mobile computers [4], [13], [15], thread blocking decisions during lock acquisition in multiprocessor applications [7], and virtual circuit holding times in IP-over-ATM networks [11], [19].
We develop a provably optimal and computationally efficient algorithm for the rent-to-buy problem. Our algorithm uses \( O(\sqrt{t}) \) time and space, and its expected cost for the t th resource use converges to optimal as \( O(\sqrt{ log t/t}) \) , for any bounded probability distribution on the resource use times. Alternatively, using O(1) time and space, the algorithm almost converges to optimal.
We describe the experimental results for the application of our algorithm to one of the motivating systems problems: the question of when to spindown a disk to save power in a mobile computer. Simulations using disk access traces obtained from an HP workstation environment suggest that our algorithm yields significantly improved power/ response time performance over the nonadaptive 2-competitive algorithm which is optimal in the worst-case competitive analysis model.
Reducing power consumption is critical in many system designs. Dynamic power management is an effective approach to decrease power without significantly degrading performance. Power management decisions can be implemented in either hardware or software. A recent trend on personal computers is to use software to change hardware power states. This paper presents a software architecture that allows system designers to investigate power management algorithms in a systematic fashion through a template. The architecture exploits the Advanced Configuration and Power Interface (ACPI), a standard for hardware and software. We implement two algorithms for controlling the power states of a hard disk on a personal computer running Microsoft Windows. By measuring the current feeding the hard disk, we show that the algorithms can save up to 25% more energy than the Windows power manager. Our work has two major contributions: a template for software-controlled power management and experimental comparisons of management algorithms for a hard disk.
