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2003, ACM Transactions on Embedded Computing Systems
Portable embedded computing systems require energy autonomy. This is achieved by batteries serving as a dedicated energy source. The requirement of portability places severe restrictions on size and weight, which in turn limits the amount of energy that is continuously available to maintain system operability. For these reasons, efficient energy utilization has become one of the key challenges to the designer of battery-powered embedded computing systems. In this paper, we first present a novel analytical battery model, which can be used for the battery lifetime estimation. The high quality of the proposed model is demonstrated with measurements and simulations. Using this battery model, we introduce a new "battery-aware" cost function, which will be used for optimizing the lifetime of the battery. This cost function generalizes the traditional minimization metric, namely the energy consumption of the system. We formulate the problem of battery-aware task scheduling on a single processor with multiple voltages. Then, we prove several important mathematical properties of the cost function. Based on these properties, we propose several algorithms for task ordering and voltage assignment, including optimal idle period insertion to exercise charge recovery. This paper presents the first effort toward a formal treatment of battery-aware task scheduling and voltage scaling, based on an accurate analytical model of the battery behavior.
Proceedings of the 39th conference on Design automation - DAC '02, 2002
Operation of battery-powered portable systems can no longer be sustained once a battery becomes discharged. Maximization of the battery lifetime is a difficult task due to nonlinearity of battery behavior that depends on the characteristics of the system load profile. We address the problem of task sequencing without and with voltage/clock scaling that shapes the profile so that the battery lifetime is maximized. We developed an accurate analytical battery model and validated it with measurements taken on a real lithium-ion battery used in a pocket computer. We use the model as a basis for a unique battery-conscious cost function and utilize its properties to develop several novel algorithms, including insertion of recovery periods and voltage/clock scaling for delay slack distribution.
IEEE Transactions on Industrial Informatics, 2010
The use of mobile devices is often limited by the battery lifetime. Some devices have the option to connect an extra battery, or to use smart battery-packs with multiple cells to extend the lifetime. In these cases, scheduling the batteries or battery cells over the load to exploit the recovery properties of the batteries helps to extend the overall systems lifetime. Straightforward scheduling schemes, like round robin or choosing the best battery available, already provide a big improvement compared to a sequential discharge of the batteries. In this paper we compare these scheduling schemes with the optimal scheduling scheme produced with two different modeling approaches: an approach based on a priced-timed automaton model (implemented and evaluated in Uppaal Cora), as well as an analytical approach (partly formulated as non-linear optimization problem) for a slightly adapted scheduling problem. We show that in some cases the results of the simple scheduling schemes (round robin, and best-first) are close to optimal. However, the optimal schedules, computed according to both methods, also clearly show that in a variety of scenarios, the simple schedules are far from optimal.
Computer, 2003
Battery Modeling for Energy-Aware System Design M any features of modern portable electronic devices-such as high-speed processors, colorful displays, optical/magnetic storage drives, and wireless network interfaces-carry a significant energy cost. However, advances in battery technology have not kept pace with rapidly growing energy demands. 1,2 Most laptops, handheld PCs, and cell phones use rechargeable electrochemical batteries-typically, lithium-ion batteries-as their portable energy source. These batteries take anywhere from 1.5 to 4 hours to fully charge, but they can run on this charge for only a few hours or, in the case of some newer pocket PCs, up to about 14 hours. The battery has thus emerged as a key parameter to control in the energy management of portables. 3-8 To meet the stringent power budget of these devices, researchers have explored various architectural, hardware, software, and system-level optimizations to minimize the energy consumed per useful computation. Maximizing the number of useful computations is effectively a problem of maximizing battery lifetime subject to system performance constraints. Given a load applied to a battery over a certain period, information about when the battery fails as well as its state of charge, or remaining capacity, at any time can be used to trade off system performance for battery lifetime at both the design stage and runtime, possibly with the user's active participation. For example, an energy-aware picture phone could let a user trade off image quality with Computationally feasible mathematical models are now available that capture battery discharge characteristics in sufficient detail to let designers develop an optimization strategy that extracts maximum charge.
2010
Abstract The use of mobile devices is often limited by the battery lifetime. Some devices have the option to connect an extra battery, or to use smart battery-packs with multiple cells to extend the lifetime. In these cases, scheduling the batteries or battery cells over the load to exploit the recovery properties of the batteries helps to extend the overall systems lifetime. Straightforward scheduling schemes, like round-robin or choosing the best battery available, already provide a big improvement compared to a sequential discharge of the batteries.
ACM Transactions on Design Automation of Electronic Systems, 2007
This paper proposes a new online voltage scaling (VS) technique for battery-powered embedded systems with real-time constraints. The VS technique takes into account the execution times and discharge currents of tasks to further reduce the battery charge consumption when compared to the recently reported slack forwarding technique , whilst maintaining low online complexity of O(1). Furthermore, we investigate the impact of online rescheduling and remapping on the battery charge consumption for tasks with data dependency which has not been explicitly addressed in the literature and propose a novel rescheduling/remapping technique. Finally, we take leakage power into consideration and extend the proposed online techniques to include adaptive body biasing (ABB) which is used to reduce the leakage power. We demonstrate and compare the efficiency of the presented techniques using seven real-life benchmarks and numerous automatically generated examples.
2009
Abstract The use of mobile devices is limited by the battery lifetime. Some devices have the option to connect an extra battery, or to use smart battery packs with multiple cells to extend the lifetime. In these cases, scheduling the batteries over the load to exploit recovery properties usually extends the system lifetime. Straightforward scheduling schemes, like round robin or choosing the best battery available, already provide a big improvement compared to a sequential discharge of the batteries.
15th Euromicro Conference on Real-Time Systems, 2003. Proceedings.
In the context of battery-powered real-time systems three constraints need to be addressed: energy, deadlines and task rewards. Many future real-time systems will count on different software versions, each with different rewards, time and energy requirements, to achieve a variety of QoS-aware tradeoffs. We propose a solution that allows the device to run the most valuable task versions while still meeting all deadlines and without depleting the energy. Assuming that the battery is rechargeable, we also propose (a) a static solution that maximizes the system value assuming a worstcase scenario (i.e., worst-case task execution times); and (b) a dynamic scheme that takes advantage of the extra energy in the system when worst-case scenarios do not happen. Three dynamic policies are shown to make better use of the recharging energy while improving the system value.
2001
Since battery life directly impacts the extent and duration of mobility, one of the key considerations in the design of a mobile embedded system should be to maximize the energy delivered by the battery, and hence the battery lifetime. To facilitate exploration of alternative implementations for a mobile embedded system, in this paper we address the issue of developing a fast and accurate battery model, and providing a framework for battery life estimation of Hardware/Software (HW/SW) embedded systems.
… , 2006. IPDPS 2006. …, 2006
Battery lifetime, a primary design constraint for mobile embedded systems, has been shown to depend heavily on the load current profile. This paper explores how scheduling guidelines from battery models can help in extending battery capacity. It then presents a 'Battery-Aware Scheduling' methodology for periodically arriving taskgraphs with real time deadlines and precedence constraints. Scheduling of even a single taskgraph while minimizing the weighted sum of a cost function has been shown to be NP-Hard . The presented methodology divides the problem in to two steps. First, a good DVS algorithms dynamically determines the minimum frequency of execution. Then, a greedy algorithm allows a near optimal priority function to choose the task which would maximize slack recovery. The methodology also ensures adherence of real time deadlines independent of the choice of the DVS algorithm and priority function used, while following battery guidelines to maximize battery lifetime. Battery simulations carried out on the profile generated by our methodology for a large set of taskgraphs show that battery life time is extended up to 23.3% as compared to existing dynamic scheduling schemes.
Proceedings 13th International Symposium on System Synthesis
Battery lifetime extension is a primary design objective for portable systems. Traditionally, battery lifetime has been prolonged mainly by reducing average power consumption of system components. A careful analysis of discharge characteristics and the adoption of accurate high-level battery models in system-level design open new opportunities for lifetime extension. In this paper, we introduce dynamic power management (DPM) policies speci cally tailored t o b attery-powered systems. Battery-driven DPM strives to enhance lifetime by automatically adapting discharge rate and current pro les to battery state-of-charge. The distinctive feature of these policies is the control of system operation based on the observation of battery output voltage. The e ectiveness of the proposed p olicies and, more in general, of the idea o f a c counting for battery behavior during system design, is proved by the experiments carried out o n a r ealistic case study, namely, an MP3 audio player.
2005
In this work we consider battery powered portable systems which either have Field Programmable Gate Arrays (FPGA) or voltage and frequency scalable processors as their main processing element. An application is modeled in the form of a precedence task graph at a coarse level of granularity. We assume that for each task in the task graph several unique design-points are available which correspond to different hardware implementations for FPGAs and different voltagefrequency combinations for processors. It is assumed that performance and total power consumption estimates for each design-point are available for any given portable platfrom, including the peripheral components such as memory and display power usage. We present an iterative heuristic algorithm which finds a sequence of tasks along with an appropriate design-point for each task, such that a deadline is met and the amount of battery energy used is as small as possible. A detailed illustrative example along with a case study of a real-world application of a robotic arm controller which demonstrates the usefulness of our algorithm is also presented.
Many portable devices rely on batteries for their power supply. The capacity of the batteries is finite, and the duration with which one can use the device is limited by the battery lifetime. Accordingly, to increase the efficiency of these systems, energy consumption and also managing the use of the batteries are too important. Given the characteristics of the nonlinear behaviour of the battery, for maximizing battery life, which is related to the discharge pattern of batteries, is one of np-hard problems. This paper to extending the system lifetime and maximizing the efficiency of the battery, presents a greedy algorithm for dynamic voltage scaling according to battery and power consumption characteristics of the tasks. These tasks have deadline and should be done on the specific time. In order to test the proposed algorithm offered in this paper, we test it with three algorithms to compare the results. Simulation results show that the proposed method (gjtbs) in different conditions (with different workload of the system) maximized systems lifetime
2001
Multi-battery power supplies are b ecoming popular in electronic appliances of the latest generations, due to economical and manufacturing constraints. Unfortunately, a partitioned b attery subsystem is not able to deliver the same amount of charge as a monolithic battery with the same total capacity. In this paper, we de ne the concept of battery scheduling, we investigate policies for solving the problem of optimal charge delivery, and we study the relationship of such policies with di erent con gurations of the battery subsystem. Results, obtained for di erent workloads, demonstrate that the choice of the proper scheduling can make, in the best case, system lifetime as close as 1% of that guaranteed by a monolithic battery of equal capacity.
IEEE Transactions on Very Large Scale Integration (VLSI) Systems, 2003
The use of multibattery power supplies is becoming common practice in electronic appliances of the latest generations. Economical and manufacturing constraints are at the basis of this choice. Unfortunately, a partitioned battery subsystem is not able to deliver the same amount of charge as a monolithic battery with the same total capacity. In this paper, we define the concept of battery scheduling, we investigate several policies for solving the problem of optimal charge delivery, and we study the relationship of such policies with different configurations of the battery subsystem. Experimental results, obtained for different kinds of current workloads, demonstrate that the choice of the proper scheduling can make system lifetime as close as 1% of the theoretical upper bound, that is, a monolithic power supply of equal capacity.
2002
Predicting the time of full discharge of a finite-capacity energy source, such as a battery, is important for the design of portable electronic systems and applications. In this paper we present a novel analytical model of a battery that not only can be used to predict battery lifetime, but also can serve as a cost function for optimization of the energy usage in battery-powered systems. The model is physically justified, and involves only two parameters, which are easily estimated. The paper includes the results of extensive experimental evaluation of the model with respect to numerical simulations of the electrochemical cell, as well as measurements taken on a real battery. The model was tested using constant, interrupted, periodic and non-periodic discharge profiles, which were derived from standard applications run on a pocket computer.
IEEE Design & Test of Computers, 2001
THE ACTIVITY OF SEVERAL COMPONENTS in a computing system is event-driven. For example, the activity of display servers, communication interfaces, and user interface functions is triggered by external events, and it is often interleaved with long, idle periods. An intuitive way to reduce average power dissipated by the whole system consists of shutting down resources during periods of inactivity. In other words, one can adopt a dynamic power management (DPM) policy that dictates how and when various components should be shut down according to a system's workload. Workload-driven DPM can be very effective, thanks to sophisticated policies, based on complex computational models (such as Markov chains) proposed in the recent literature. 1 We observe, however, that minimum average power is not always the objective when designing battery-operated, mobile applications. Rather, what really matters for this kind of system is ensuring long battery lifetime. Average power reduction and battery lifetime Battery-Driven Dynamic Power Management Battery lifetime extension is a primary design objective for portable systems. We introduce the concept of battery-driven dynamic power management, which strives to enhance lifetime by automatically adapting discharge rate and current profiles to battery charge state.
Proceedings of the 2018 on Great Lakes Symposium on VLSI (GLSVLSI), 2018
Lifetime maximization is a key challenge in battery-powered multi-sensor devices. Battery-aware power management strategies combine task scheduling with dynamic voltage scaling (DVS), accounting for the fact that the power drawn by the device is different from that provided by the battery due to its many non-idealities. However, state-of-the-art techniques in this field do not take into account several important aspects, such as the impact of sensing tasks on the overall power demand, the (operating point dependent) losses due to multiple DC-DC conversions, and the dynamic modifications in battery efficiency caused by different distributions of the currents in the temporal and in the frequency domains. In this work, we propose a novel approach to identify optimal power management solutions, that addresses all these limitations. Specifically, using advanced battery and DC-DC converter models, we propose methods to explore the scheduling space both statically (at design time) and dynamically (at runtime), accounting not only for computation tasks, but also for communication and sensing. With this method, we show that the battery lifetime can be increased by as much as 23.36% if an optimal power management strategy is adopted.
Proceedings of the 43rd annual conference on Design automation - DAC '06, 2006
Most existing dynamic voltage scaling (DVS) schemes for multiple tasks assume an energy cost function (energy consumption versus execution time) that is independent of the task characteristics. In practice the actual energy cost functions vary significantly from task to task. Different tasks running on the same hardware platform can exhibit different memory and peripheral access patterns, cache miss rates, etc. These effects results in a distinct energy cost function for each task. We present a new formulation and solution to the problem of minimizing the total (dynamic and static) system energy while executing a set of tasks under DVS. First, we demonstrate and quantify the dependence of the energy cost function on task characteristics by direct measurements on a real hardware platform (the TI OMAP processor) using real application programs. Next, we present simple analytical solutions to the problem of determining energy-optimal voltage scale factors for each task, while allowing each task to be preempted and to have its own energy cost function. Based on these solutions, we present simple and efficient algorithms for implementing DVS with multiple tasks. We consider two cases: (1) all tasks have a single deadline, and (2) each task has its own deadline. Experiments on a real hardware platform using real applications demonstrate a 10% additional saving in total system energy compared to previous leakage-aware DVS schemes.
IEEE/ACM International Conference on Computer Aided Design. ICCAD 2001. IEEE/ACM Digest of Technical Papers (Cat. No.01CH37281), 2001
Once the battery becomes fully discharged, a battery-powered portable electronic system goes off-line. Therefore, it is important to take the battery behavior into account. A system designer needs an adequate high-level model in order to make battery-aware decisions that target maximization of the system's lifetime on-line. We propose such a model: it allows a designer to predict the battery time-to-failure for a given load and provides a cost metric for lifetime optimization algorithms. Our model also allows for a tradeoff between the accuracy and the amount of computation performed. The quality of the proposed model is evaluated using a detailed low-level simulation of a lithium-ion electrochemical cell.
2005 IEEE International Symposium on Circuits and Systems, 2005
In a battery powered system, a primary design consideration is the battery lifetime. Profile of current drawn from a battery determines its lifetime. Recently in [4] dynamic voltage scaling has been applied to alter the battery load current profile in distributed systems to reduce battery charge consumption. Load current profile is changed by utilizing the slack in the execution of the scheduled tasks. In this paper we propose a new dynamic voltage scaling procedure that alters load current profile by considering the total battery current instead of the method of [4] that considers the current dawn by individual task with the latest finish times in the schedule. The task schedule is partitioned into steps defined in this work and the load currents during selected steps are targeted for reduction by scaling the supply voltage of the processing elements. Experimental results on a large set of task graphs show that battery charge consumption reductions of up to 89.80% are achieved by the new algorithm.
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