MIMOSA, a Highly Sensitive and Accurate Power Measurement Technique for Low-Power Systems

Buschhoff, Markus; Gunter, Christian; Spinczyk, Olaf

doi:10.1007/978-3-319-03071-5_16

Cited by 5 publications

(4 citation statements)

References 4 publications

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“…They used for that purpose a sensor node management device, sampling the mote current at 1000 Hz. Recently another device, with even better resolution has been presented at the RealWSN workshop in Como [4] for similar purposes. However, with modern radios such as the CC2420 [5] these high frequency data loggers are quite useless as the radio draws its energy from a large capacitor that is replenished from the external batteries whenever needed by a built-in DC to DC converter.…”

Section: Powermentioning

confidence: 97%

Experimental evaluation of message latency and power usage in WSNs

Uwase

Bezunartea

Nguyen

et al. 2014

2014 IEEE International Black Sea Conference on Communications and Networking (BlackSeaCom)

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Different techniques for measuring packet delivery ratio, packet latency and power usage on single hop radio links have been developed. These techniques were tested with Zolertia Z1 motes running Contiki by measuring the performances of different radio duty cycling protocols using a range of radio wake-up rates and various levels of global radio traffic. Advantages and disadvantages of the different techniques are discussed and the feasibility of enhancing them for multihop networks is explored.

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Section: Powermentioning

confidence: 97%

Experimental evaluation of message latency and power usage in WSNs

Uwase

Bezunartea

Nguyen

et al. 2014

2014 IEEE International Black Sea Conference on Communications and Networking (BlackSeaCom)

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“…Profiling uses power models during early design phases, and shifts to real-hardware measurements postimplementation. While standard power profiling methods are available for HPClike systems [16], power profiling in embedded systems remains largely ad-hoc [17,26,2,4,13].…”

Section: Power Measurement Utility (Pmu)mentioning

confidence: 99%

Supporting Utilities for Heterogeneous Embedded Image Processing Platforms (STHEM): An Overview

Sadek

Muddukrishna

Kalms

et al. 2018

Applied Reconfigurable Computing. Architectures, Tools, and Applications

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The TULIPP project aims to simplify development of embedded vision applications with low-power and real-time requirements by providing a complete image processing system package called the TULIPP Starter Kit. To achieve this, the chosen high-performance embedded vision platform needs to be extended with performance analysis and power measurement features. The lack of such features plagues most embedded vision platforms in general and practitioners have adopted adhoc methods to circumvent the problem. In this paper, we describe four generic utilities that complement and refine the capabilities of existing platforms for embedded vision applications. Concretely, we describe a novel power measurement and analysis utility, a platform-optimized image processing library, a dynamic partial reconfiguration utility, and an utility providing support for using the real-time OS HIPPEROS within Xilinx SDSoC. Collectively, these utilities enable efficient development of image processing applications on the TULIPP hardware platform. In future work, we will evaluate the relative benefit of these utilities on key embedded image processing metrics such as frame rate and power consumption.

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“…The sample rate can be raised by using higher value sampling devices like oscilloscopes right behind the sensing unit. In comparison to existing commercial devices for energy measurement in embedded systems, MIMOSA is able to represent the signal form more precisely without loss of overall accuracy [10]. On the downside, MIMOSA requires a more sophisticated setup as it is necessary to replace the constant voltage source of the DUT.…”

Section: Figure 9amentioning

confidence: 99%

“…Buschhoff et al [10] proposed MIMOSA 3 , a measurement device for creating high-accuracy energy models of embedded system components. MIMOSA combines different measurement approaches.…”

Section: Mimosamentioning

confidence: 99%

Measuring Energy

Kerrison¹,

Buschhoff²,

Nunez-Yanez³

et al. 2017

ICT - Energy Concepts for Energy Efficiency and Sustainability

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This chapter provides an introduction to quantifying the energy consumed by software. It is written for computer scientists, software engineers, embedded system developers and programmers who want to understand how to measure the energy consumed by the code they write in order to optimize for energy efficiency. We start with an overview of the electrical foundations of energy measurement and show how these are applied by reviewing the most commonly found energy sensing techniques. This is followed by a brief discussion of the signal processing required to obtain energy consumption data from sensing. We then present two energy measurement systems that are based on sensing techniques. Both can be used to directly measure the energy consumed by software running on embedded systems without the need to modify the hardware. As an alternative, regression-based techniques can be used to infer energy consumption based on monitoring events during program execution using counters monitors offered by the hardware. We introduce the foundations of regression analysis and illustrate how an energy model for an ARM processor can be built using linear regression. In the conclusion, we offer a wider discussion on what should be considered when selecting an energy measurement technique.

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MIMOSA, a Highly Sensitive and Accurate Power Measurement Technique for Low-Power Systems

Cited by 5 publications

References 4 publications

Experimental evaluation of message latency and power usage in WSNs

Experimental evaluation of message latency and power usage in WSNs

Supporting Utilities for Heterogeneous Embedded Image Processing Platforms (STHEM): An Overview

Measuring Energy

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