Fast and Accurate Embedded Systems Energy Characterization Using Non-intrusive Measurements

International audienceIn this paper we propose a complete system energy model based on non-intrusive measurements. This model aims at being integrated in fast cycle accurate simulation tools to give energy consumption feedback for embedded systems software design. Estimations takes into account the whole system consumption including peripherals. Experiments on a complex ARM9 platform show that our model estimates are in error by less than 10% from real system consumption, which is precise enough for source code application design, while simulation speed remains fast

Measuring and modelling the energy cost of reconfiguration in sensor networks [forthcoming]

As Wireless Sensor Networks (WSN) must operate for long periods on a limited power budget, estimating the energy cost of software operations is critical. Contemporary reconfiguration approaches for WSN allow for software evolution at various granularities; from reflashing of a complete software image, through replacement of complete applications, to the reconfiguration of individual software components. This paper contributes a generic model for measuring and modelling the energy cost of reconfiguration in WSN. We validate that this model is accurate in the face of different hardware platforms, software stacks and software encapsulation approaches. We have embedded this model in the LooCI middleware, resulting in the first energy aware reconfigurable component model for sensor networks. We evaluate our approach using two real-world WSN applications and demonstrate that our model predicts the energy cost of reconfiguration with 93% accuracy. Using this model we demonstrate that selecting the most appropriate software modularisation approach is key to minimising energy consumption

System Energy Assessment (SEA), Defining a Standard Measure of EROI for Energy Businesses as Whole Systems

A more objective method for measuring the energy needs of businesses, System Energy Assessment (SEA), identifies the natural boundaries of businesses as self-managing net-energy systems, of controlled and self-managing parts. The method is demonstrated using a model Wind Farm case study, and applied to defining a true physical measure of its energy productivity for society (EROI-S), the global ratio of energy produced to energy cost. The traceable needs of business technology are combined with assignable energy needs for all other operating services. That serves to correct a large natural gap in energy use information. Current methods count traceable energy receipts for technology use. Self-managing services employed by businesses outsource their own energy needs to operate, and leave no records to trace. Those uncounted energy demands are often 80% of the total embodied energy of business end products. The scale of this "dark energy" was discovered from differing global accounts, and corrected so the average energy cost per dollar for businesses would equal the world average energy use per dollar of GDP. Presently the energy needs of paid services that outsource their own energy needs are counted for lack of information to be "0". Our default assumption is to treat them as "average". The result is to assign total energy use and impacts to the demand for energy services, for a "Scope 4" GHG assessment level. Counting only the energy uses of technology understates the energy needs of business services, as if services were more energy efficient than technology. The result confirms a similar finding by Hall et. al. in 1981 [9]. We use exhaustive search for what a business needs to operate as a whole, locating a natural physical boundary for its working parts, to define businesses as physical rather than statistical subjects of science. :measurement, natural systemsComment: 33 pages, 15 figures, accepted as part of pending special issue on EROI organized by Charlie Hall for Sustainability (MDPI

An analytical methodology to derive power models based on hardware and software metrics

The use of models to predict the power consumption of a system is an appealing alternative to wattmeters since they avoid hardware costs and are easy to deploy. In this paper, we present an analytical methodology to build models with a reduced number of features in order to estimate power consumption at node level. We aim at building simple power models by performing a per-component analysis (CPU, memory, network, I/O) through the execution of four standard benchmarks. While they are executed, information from all the available hardware counters and resource utilization metrics provided by the system is collected. Based on correlations among the recorded metrics and their correlation with the instantaneous power, our methodology allows (i) to identify the significant metrics; and (ii) to assign weights to the selected metrics in order to derive reduced models. The reduction also aims at extracting models that are based on a set of hardware counters and utilization metrics that can be obtained simultaneously and, thus, can be gathered and computed on-line. The utility of our procedure is validated using real-life applications on an Intel Sandy Bridge architecture

In-field Built-in Self-test for Measuring RF Transmitter Power and Gain

abstract: RF transmitter manufacturers go to great extremes and expense to ensure that their product meets the RF output power requirements for which they are designed. Therefore, there is an urgent need for in-field monitoring of output power and gain to bring down the costs of RF transceiver testing and ensure product reliability. Built-in self-test (BIST) techniques can perform such monitoring without the requirement for expensive RF test equipment. In most BIST techniques, on-chip resources, such as peak detectors, power detectors, or envelope detectors are used along with frequency down conversion to analyze the output of the design under test (DUT). However, this conversion circuitry is subject to similar process, voltage, and temperature (PVT) variations as the DUT and affects the measurement accuracy. So, it is important to monitor BIST performance over time, voltage and temperature, such that accurate in-field measurements can be performed. In this research, a multistep BIST solution using only baseband signals for test analysis is presented. An on-chip signal generation circuit, which is robust with respect to time, supply voltage, and temperature variations is used for self-calibration of the BIST system before the DUT measurement. Using mathematical modelling, an analytical expression for the output signal is derived first and then test signals are devised to extract the output power of the DUT. By utilizing a standard 180nm IBM7RF CMOS process, a 2.4GHz low power RF IC incorporated with the proposed BIST circuitry and on-chip test signal source is designed and fabricated. Experimental results are presented, which show this BIST method can monitor the DUT’s output power with +/- 0.35dB accuracy over a 20dB power dynamic range.Dissertation/ThesisMasters Thesis Electrical Engineering 201

Energy aware software evolution for wireless sensor networks

Wireless Sensor Networks (WSNs) are subject to high levels of dynamism arising from changing environmental conditions and application requirements. Reconfiguration allows software functionality to be optimized for current environmental conditions and supports software evolution to meet variable application requirements. Contemporary software modularization approaches for WSNs allow for software evolution at various granularities; from monolithic re-flashing of OS and application functionality, through replacement of complete applications, to the reconfiguration of individual software components. As the nodes that compose a WSN must typically operate for long periods on a single battery charge, estimating the energy cost of software evolution is critical. This paper contributes a generic model for calculating the energy cost of the reconfiguration in WSN. We have embedded this model in the LooCI middleware, resulting in the first energy aware reconfigurable component model for sensor networks. We evaluate our approach using two real-world WSN applications and find that (i.) our model accurately predicts the energy cost of reconfiguration and (ii.) component-based reconfiguration has a high initial cost, but provides energy savings during software evolution