Optimal Power-Down Strategies

We consider the problem of selecting threshold times to transition a device to low-power sleep states during an idle period. The two-state case, in which there is a single active and a single sleep state, is a continuous version of the ski-rental problem. We consider a generalized version in which there is more than one sleep state, each with its own power-consumption rate and transition costs. We give an algorithm that, given a system, produces a deterministic strategy whose competitive ratio is arbitrarily close to optimal. We also give an algorithm to produce the optimal online strategy given a system and a probability distribution that generates the length of the idle period. We also give a simple algorithm that achieves a competitive ratio of $3 + 2\sqrt{2} \approx 5.828$ for any system

Stochastic Learning Feedback Hybrid Automata for Power Management in Embedded Systems

In this paper we show that stochastic learning automata based feedback control switching strategy can be used for dynamic power management (DPM) employed at the system level. DPM strategies are usually incorporated at the operating systems of embedded devices to exploit multiple power states available in today\u27s ACPI compliant devices. The idea is to switch between power states depending on the device usage, and since device usage times are not deterministic, probabilistic techniques are often used to create stochastic strategies, or strategies that make decisions based on probabilities of device usage spans. Previous work (Irani et al., 2001) has shown how to approximate the probability distribution of device idle times and dynamically update them, and then use such knowledge in controlling power states. Here, we use stochastic learning automata (SLA) which interacts with the environment to update such probabilities, and then apply techniques similar to (Irani et al., 2001) to optimize power usage with minimal effect on response time for the devices

Stochastic Learning Feedback Hybrid Automata for Dynamic Power Management in Embedded Systems

Dynamic power management (DPM) refers to the strategies employed at system level to reduce energy expenditure (i.e. to prolong battery life) in embedded systems. The trade-off involved in DPM techniques is between the reductions of energy consumption and latency suffered by the tasks. Such trade-offs need to be decided at runtime, making DPM an on-line problem. We formulate DPM as a hybrid automaton control problem and integrate stochastic control. The control strategy is learnt dynamically using stochastic learning hybrid automata (SLHA) with feedback learning algorithms. Simulation-based experiments show the expediency of the feedback systems in stationary environments. Further experiments reveal that SLHA attains better trade-offs than several former predictive algorithms under certain trace data

Energy-aware embedded media processing: customizable memory subsystems and energy management policies

textThe design of energy-efficient data memory architectures for embedded system platforms has received considerable attention in recent years. In this dissertation we propose a special-purpose data memory subsystem, called Xtream-Fit, targeted to streaming media applications executing on both generic uniprocessor embedded platforms and powerful SMT-based multi-threading platforms. We empirically demonstrate that Xtream-Fit achieves high energydelay efficiency across a wide range of media devices, from systems running a single media application to systems concurrently executing multiple media applications under synchronization constraints. Xtream-Fit’s energy efficiency is predicated on a novel task-based execution model that exposes/enhances opportunities for efficient prefetching, and aggressive dynamic energy conservation techniques targeting on-chip and off-chip memory components. A key novelty of Xtream-Fit is that it exposes a single customization parameter, thus enabling a very simple and yet effective design space exploration methodology to find the best memory configuration for the target application(s). Extensive experimental results show that Xtream-Fit reduces energy-delay product substantially – by 32% to 69% – as compared to ‘standard’ general-purpose memory subsystems enhanced with state of the art cache decay and SDRAM power mode control policies.Electrical and Computer Engineerin

Energy Efficient Designs for Collaborative Signal and Information Processing inWireless Sensor Networks

Collaborative signal and information processing (CSIP) plays an important role in the deployment of wireless sensor networks. Since each sensor has limited computing capability, constrained power usage, and limited sensing range, collaboration among sensor nodes is important in order to compensate for each other’s limitation as well as to improve the degree of fault tolerance. In order to support the execution of CSIP algorithms, distributed computing paradigm and clustering protocols, are needed, which are the major concentrations of this dissertation. In order to facilitate collaboration among sensor nodes, we present a mobile-agent computing paradigm, where instead of each sensor node sending local information to a processing center, as is typical in the client/server-based computing, the processing code is moved to the sensor nodes through mobile agents. We further conduct extensive performance evaluation versus the traditional client/server-based computing. Experimental results show that the mobile agent paradigm performs much better when the number of nodes is large while the client/server paradigm is advantageous when the number of nodes is small. Based on this result, we propose a hybrid computing paradigm that adopts different computing models within different clusters of sensor nodes. Either the client/server or the mobile agent paradigm can be employed within clusters or between clusters according to the different cluster configurations. This new computing paradigm can take full advantages of both client/server and mobile agent computing paradigms. Simulations show that the hybrid computing paradigm performs better than either the client/server or the mobile agent computing. The mobile agent itinerary has a significant impact on the overall performance of the sensor network. We thus formulate both the static mobile agent planning and the dynamic mobile agent planning as optimization problems. Based on the models, we present three itinerary planning algorithms. We have showed, through simulation, that the predictive dynamic itinerary performs the best under a wide range of conditions, thus making it particularly suitable for CSIP in wireless sensor networks. In order to facilitate the deployment of hybrid computing paradigm, we proposed a decentralized reactive clustering (DRC) protocol to cluster the sensor network in an energy-efficient way. The clustering process is only invoked by events occur in the sensor network. Nodes that do not detect the events are put into the sleep state to save energy. In addition, power control technique is used to minimize the transmission power needed. The advantages of DRC protocol are demonstrated through simulations

Networks-on-Chip: An Interconnect Fabric for Multiprocessor Systems-on-Chip

The first self-contained volume of the Embedded Systems Handbook is entitled Embedded Systems Design and Verification. Comprised of the contributions of experts directly involved in the technologies presented, this text is divided into three sections. It begins with a brief introduction to embedded systems design and verification. It then provides a comprehensive overview of embedded processors and various aspects of system-on-chip and FPGA, as well as solutions to design challenges. The final section explores power-aware embedded computing, design issues specific to secure embedded systems, and web services for embedded devices

Timing and Energy Estimation of C Programs [SPECIAL ISSUE ON POWER AWARE EMBEDDED COMPUTING]

This paper affords the problem of analyzing the timing and energetic aspects of software for embedded applications. The main goal of the approach is to enable design space exploration over different microprocessors, development environments and coding alternatives. The approach embodies the benefits of static and dynamic analysis within a formal mathematical framework and takes full advantage of the accuracy of low–level methodologies while operating at source code level. The experimental assessment of the methodology considered C programs derived from real–world applications and confirmed its accuracy and effectiveness

Embedded systems handbook / edited by Richard Zurawski.

Includes bibliographical references and indexes.Book fair 2012.2 v. :Considered a standard industry resource, the Embedded Systems Handbook provided researchers and technicians with the authoritative information needed to launch a wealth of diverse applications, including those in automotive electronics, industrial automated systems, and building automation and control. Now a new resource is required to report on current developments and provide a technical reference for those looking to move the field forward yet again. Divided into two volumes to accommodate this growth, the Embedded Systems Handbook, Second Edition presents a comprehensive view on this area of computer engineering with a currently appropriate emphasis on developments in networking and applications. Those experts directly involved in the creation and evolution of the ideas and technologies presented offer tutorials, research surveys, and technology overviews that explore cutting-edge developments and deployments and identify potential trends. This first self-contained volume of the handbook, Embedded Systems Design and Verification, is divided into three sections. It begins with a brief introduction to embedded systems design and verification. It then provides a comprehensive overview of embedded processors and various aspects of system-on-chip and FPGA, as well as solutions to design challenges. The final section explores power-aware embedded computing, design issues specific to secure embedded systems, and web services for embedded devices