Energy Consumption Estimation in Embedded Systems

Konstantakos,; Chatzigeorgiou,; Nikolaidis,; Laopoulos,

doi:10.1109/imtc.2006.328405

Cited by 17 publications

(6 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Research presented in [14]- [17] introduce advanced measurement systems that are capable to measure energy consumed between two cycles, but still, an appropriate discussion about static and dynamic power consumption measurement was missing.…”

Section: Related Papersmentioning

confidence: 99%

An Empirical Methodology for Power Analysis of CMOS Integrated Circuits

Krunić¹,

Povazan²,

Kovačević³

et al. 2017

ElAEE

View full text Add to dashboard Cite

Energy consumption is becoming one of the most significant aspects of CMOS Integrated Circuits (IC), especially for those applied in embedded devices whose autonomy depends upon battery lifespan. Therefore, an empirical methodology for determination of power and energy dissipation may provide valuable information to IC designers, as well as software developers, which could impact design process and lead to more energy-efficient solutions. This paper presents a novel methodology for determination of static and dynamic components of energy dissipation for those CMOS ICs that do not support turning off clock distribution entirely, but provide ability to divide a clock frequency. For that purpose, we used an Eclipse based IDE that provides a user friendly interface for dividing a clock frequency on ultra-low power embedded DSP platform, which was used as a target device. Measurements were performed using a true RMS multimeter. Various experiments were conducted using different scenarios, on single and multi cores, in order to validate the described empirical methodology, and the outcome confirmed what was expected, that the obtained results are stable and accurate.

show abstract

Section: Related Papersmentioning

confidence: 99%

An Empirical Methodology for Power Analysis of CMOS Integrated Circuits

Krunić¹,

Povazan²,

Kovačević³

et al. 2017

ElAEE

View full text Add to dashboard Cite

show abstract

“…Energy predictive models. The first approach using the external power meters is considered to be accurate [12]. It can provide only the measurement at a computer level and cannot, therefore, provide the fine-grained decomposition of the energy consumption by an application executing on multiple independent computing devices in a computer.…”

Section: Comparative Analysis Of the State-of-the-art Energy Meamentioning

confidence: 99%

Accurate Energy Modelling of Hybrid Parallel Applications on Modern Heterogeneous Computing Platforms Using System-Level Measurements

et al. 2020

View full text Add to dashboard Cite

Modern high-performance computing platforms, cloud computing systems, and data centers are highly heterogeneous containing nodes where a multicore CPU is tightly integrated with accelerators. An important challenge for energy optimization of hybrid parallel applications on such platforms is how to accurately estimate the energy consumption of application components running on different compute devices of the platform. In this work, we propose a method for accurate estimation of the application component-level energy consumption employing system-level power measurements with power meters. We experimentally validate the method on a cluster of two hybrid heterogeneous computing nodes using three parallel applications -matrix-matrix multiplication, 2D fast Fourier transform and gene sequencing. The experiments demonstrate a high estimation accuracy of the proposed method, with the average estimation error ranging between 2% and 5%. The average error demonstrated by the state-of-the-art estimation methods for the same experimental setup ranges from 15% to 75%, while the maximum reaches 178%. We also show that the use of the state-of-the-art estimation methods instead of the proposed one in the energy optimization loop leads to significant energy losses (up to 45% in our case). INDEX TERMS Energy modelling, energy optimization, power meters, on-chip power sensors, heterogeneous platforms, parallel applications, multicore CPU, GPU, Intel Xeon Phi, HPC.

show abstract

“…In addition to communication aspects, models have been proposed for modeling the energy consumption of processors [50], [51]. After accurate energy profiling of processor and peripherals, the energy cost of each instruction is used to calculate the energy of applications.…”

Section: B Analytical Estimationmentioning

confidence: 99%

EMPIOT: An energy measurement platform for wireless IoT devices

Dezfouli

Amirtharaj

2018

Journal of Network and Computer Applications

View full text Add to dashboard Cite

Profiling and minimizing the energy consumption of resource-constrained devices is an essential step towards employing IoT in various application domains. Due to the large size and high cost of commercial energy measurement platforms, alternative solutions have been proposed by the research community. However, the three main shortcomings of existing tools are complexity, limited measurement range, and low accuracy. Specifically, these tools are not suitable for the energy measurement of new IoT devices such as those supporting the 802.11 technology. In this paper we propose EMPIOT, an accurate, low-cost, easy to build, and flexible power measurement platform. We present the hardware and software components of this platform and study the effect of various design parameters on accuracy and overhead. In particular, we analyze the effects of driver, bus speed, input voltage, and buffering mechanism on sampling rate, measurement accuracy and processing demand. These extensive experimental studies enable us to configure the system in order to achieve its highest performance. We also propose a novel calibration technique and report the calibration parameters under various settings. Using five different IoT devices performing four types of workloads, we evaluate the performance of EMPIOT against the ground truth obtained from a high-accuracy industrial-grade power measurement tool. Our results show that, for very low-power devices that utilize 802.15.4 wireless standard, the measurement error is less than 3.5%. In addition, for 802.11based devices that generate short and high power spikes, the error is less than 2.5%.

show abstract

Energy Consumption Estimation in Embedded Systems

Cited by 17 publications

References 5 publications

An Empirical Methodology for Power Analysis of CMOS Integrated Circuits

An Empirical Methodology for Power Analysis of CMOS Integrated Circuits

Accurate Energy Modelling of Hybrid Parallel Applications on Modern Heterogeneous Computing Platforms Using System-Level Measurements

EMPIOT: An energy measurement platform for wireless IoT devices

Contact Info

Product

Resources

About