A Survey of Techniques For Improving Energy Efficiency in Embedded Computing Systems

Recent technological advances have greatly improved the performance and features of embedded systems. With the number of just mobile devices now reaching nearly equal to the population of earth, embedded systems have truly become ubiquitous. These trends, however, have also made the task of managing their power consumption extremely challenging. In recent years, several techniques have been proposed to address this issue. In this paper, we survey the techniques for managing power consumption of embedded systems. We discuss the need of power management and provide a classification of the techniques on several important parameters to highlight their similarities and differences. This paper is intended to help the researchers and application-developers in gaining insights into the working of power management techniques and designing even more efficient high-performance embedded systems of tomorrow

A composable, energy-managed, real-time MPSOC platform

Multi-processors systems on chip (MPSOC) platforms emerged in embedded systems as hardware solutions to support the continuously increasing functionality and performance demands in this domain. Such a platform has to execute a mix of applications with diverse performance and timing constraints, i.e., real-time or non-real-time, thus different application schedulers should co-exist on an MPSOC. Moreover, applications share many MPSOC resources, thus their timing depends on the arbitration at these resources. Arbitration may create inter-application dependencies, e.g., the timing of a low priority application depends on the timing of all higher priority ones. Application inter-dependencies make the functional and timing verification and the integration process harder. This is especially problematic for real-time applications, for which fulfilling the time-related constraints should be guaranteed by construction. Moreover, energy and power management, commonly employed in embedded systems, make this verification even more difficult. Typically, energy and power management involves scaling the resources operating point, which has a direct impact on the resource performance, thus influences the application time behaviour. Finally, a small change in one application leads to the need to re-verify all other applications, incurring a large effort. Composability is a property meant to ease the verification and integration process. A system is composable if the functionality and the timing behaviour of each application is independent of other applications mapped on the same platform. Composability is achieved by utilising arbiters that ensure applications independence. In this paper we present the concepts behind a composable, scalable, energy-managed MPSOC platform, able to support different real-time and nonreal time schedulers concurrently, and discuss its advantages and limitations

Schedulability Analysis of Real-Time Systems with Uncertain Worst-Case Execution Times

Schedulability analysis is about determining whether a given set of real-time software tasks are schedulable, i.e., whether task executions always complete before their specified deadlines. It is an important activity at both early design and late development stages of real-time systems. Schedulability analysis requires as input the estimated worst-case execution times (WCET) for software tasks. However, in practice, engineers often cannot provide precise point WCET estimates and prefer to provide plausible WCET ranges. Given a set of real-time tasks with such ranges, we provide an automated technique to determine for what WCET values the system is likely to meet its deadlines, and hence operate safely. Our approach combines a search algorithm for generating worst-case scheduling scenarios with polynomial logistic regression for inferring safe WCET ranges. We evaluated our approach by applying it to a satellite on-board system. Our approach efficiently and accurately estimates safe WCET ranges within which deadlines are likely to be satisfied with high confidence

Optimizing Constrainted Concurrent Applications at Run-time

Computer systems are resource constrained. Application adaptation is a useful way to optimize system resource usage while satisfying an application’s performance requirements. Current multicore computer systems supporting these applications, however, are not designed to reliably meet these requirements. Meanwhile, these computer systems are resource-limited, e.g., have power-induced energy and thermal constraints. Compounding the application’s performance requirements are increasingly-stringent microprocessor thermal constraints. Previous application adaptation efforts, however, were ad-hoc, time-consuming, and highly application-specific, with limited portability between computer systems. This thesis presents OCCAM, a software platform for developing multicore adaptable applications. OCCAM’s design-time platform consists of design patterns, APIs, and data structures that allow application developers to specify the performance constraints and application-specific optimization techniques. OCCAM generates a run-time controller offline, using profiling data. It then uses this profiling data to generate an internal model that it subsequently employs to generate a robust Markov Decision Process-based Model Predictive Controller. Using a set of Recognition, Mining, and Synthesis benchmarks, the experimental study demonstrates that OCCAM can successfully optimize the system while meeting the systems performance requirements across a wide range of computer platforms, ranging from an energy-constrained single-core system to a high-performance 16-core system. Finally, OCCAM presents a simulation-based, stochastic model checking-based framework for quantifying the robustness of the controller