As embedded systems get more complex, deployment of embedded operating systems (OSs) as software run-time engines has become common. In particular, this trend is true even for battery-powered embedded systems, where maximizing battery life is a primary concern. In such OS-driven embedded software, the overall energy consumption depends very much on which OS is used and how the OS is used. Therefore, the energy effects of the OS need to be studied in order to design low-energy systems effectively.In this paper, we discuss the motivation for performing OS energy characterization and propose a methodology to perform the characterization systematically. The methodology consists of two parts. The first part is analysis , which is concerned with identifying a set of components that can be used to characterize the OS energy consumption, called energy characteristics . The second part is macromodeling , which is concerned with obtaining quantitative macromodels for the energy characteristics. It involves the process of experiment design, data collection, and macromodel fitting. The OS energy macromodels can be used conveniently as OS energy estimators in high-level or architectural optimization of embedded systems for low-energy consumption.As far as we know, this work is the first attempt to systematically tackle energy macromodeling of an embedded OS. To demonstrate our approach, we present experimental results for two well-known embedded OSs, namely, μC/OS and embedded Linux OS.

In this paper, we describe a comprehensive high-level synthesis system for control-flow intensive as well as data-dominated behaviors. We propose a new control-data flow graph model to preserve the parallelism inherent in the application, as well as to facilitate high-level synthesis. Our algorithm, which is based on an iterative improvement strategy, performs clock selection, scheduling, module selection, resource allocation and assignment simultaneously to fully derive the benefits of design space exploration at the behavior level. The system can be used to optimize area, power or energy, by selecting the cost function accordingly. Experimental results show that for energy-optimized designs, energy is reduced by up to 79.4% (an average of 42.2%), with an average of 24.8% area overhead, compared to area-optimized designs. For power-optimized designs, power is reduced by up to 70.8% (an average of 56.7%), with an average of 25.2% area overhead, compared to area-optimized designs. No Vdd scaling is performed to obtain the above results.

Energy consumption has become a major focus in the design of embedded systems (e.g., mobile computing and wireless communication devices). In particular, a shift of emphasis from hardware-oriented low-energy design techniques to energy-efficient embedded software design has occurred progressively in the past few years. To that end, various techniques have been developed for the design of energy-efficient embedded software. In operating system (OS)-driven embedded systems, the OS has a significant impact on the system's energy consumption directly (energy consumption associated with the execution of the OS functions and services), as well as indirectly (interaction of the OS with the application software). As a first step toward designing energy-efficient OS-based embedded systems, it is important to analyze the energy consumption of embedded software by taking the OS energy characteristics into account. To facilitate such studies, we present, in this work, an energy simulation framework that can be used to analyze the energy consumption characteristics of an embedded system featuring the embedded Linux OS running on the StrongARM processor. The framework allows software designers to study the energy consumption of the system software in relation to the application software, identify the energy hot spots, and perform design changes based on the knowledge of the OS energy consumption characteristics as well as application-OS interactions.

A large and increasing number of modern embedded systems are subject to tight power/energy constraints. It has been demonstrated that the operating system (OS) can have a significant impact on the energy efficiency of the embedded system. Hence, analysis of the energy effects of the OS is of great importance. Conventional approaches to energy analysis of the OS (and embedded software, in general) require the application software to be completely developed and integrated with the system software, and that either measurement on a hardware prototype or detailed simulation of the entire system be performed. Since this process requires significant design effort, unfortunately, it is typically too late in the design cycle to perform high-level or architectural optimizations on the embedded software, restricting the scope of power savings. Our work recognizes the need to provide embedded software designers with feedback about the effect of different OS services on energy consumption early in the design cycle. As a first step in that direction, this paper presents a systematic methodology to perform energy analysis and macro-modeling of an embedded OS. Our energy macro-models provide software architects and developers with an intuitive model for the OS energy effects, since they directly associate energy consumption with OS services and primitives that are visible to the application software. Our methodology consists of (i) an analysis stage, where we identify a set of energy components, called energy characteristics, which are useful to the designer in making OS-related design trade-offs, and (ii) a subsequent macromodeling stage, where we collect data for the identified energy components and automatically derive macro-models for them. We validate our methodology by deriving energy macro-models for two state-of-the-art embedded OS's, /spl mu/C/OS and Linux OS.

Presents an efficient and accurate high level software energy estimation methodology using the concept of characterization-based macromodeling. In characterization-based macromodeling, a function or subroutine is characterized using an accurate lower level energy model of the target processor to construct a macromodel that relates the energy consumed in the function under consideration to various parameters that can be easily observed or calculated from a high-level programming language description. The constructed macromodels eliminate the need for significantly slower instruction-level interpretation or hardware simulation that is required in conventional approaches to software energy estimation. Two different approaches to macromodeling for embedded software offer distinct efficiency-accuracy characteristics: 1) complexity-based macromodeling, where the variables that determine the algorithmic complexity of the function under consideration are used as macromodeling parameters and 2) profiling-based macromodeling, where internal profiling statistics for the functions are used as the parameters in the energy macromodels.

This paper presents an efficient and accurate high-level software energy estimation methodology using the concept of characterization-based macro-modeling. In characterization-based macro-modeling, a function or sub-routine is characterized using an accurate lower-level energy model of the target processor, to construct a macro-model that relates the energy consumed in the function under consideration to various parameters that can be easily observed or calculated from a high-level programming language description. The constructed macro-models eliminate the need for significantly slower instruction-level interpretation or hardware simulation that is required in conventional approaches to software energy estimation.