Unified Power Modeling Design for Various Raspberry Pi Generations Analyzing Different Statistical Methods

Paniego, Juan Manuel; Libutti, Leandro; Puig, Martín Pi; Chichizola, Franco; Giusti, Laura Cristina De; Naiouf, Marcelo; Giusti, Armando De

doi:10.1007/978-3-030-48325-8_4

Cited by 7 publications

(7 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Leading on from work by Paniego et al in [17], Paniego et al in [23] expand on their previous findings and consider the power consumption characteristics of a Raspberry Pi3 + generation of board. Their analysis details the differences in power consumption between the Raspberry Pi3 and Raspberry Pi3 + , including the differences based on the number of threads executed.…”

Section: Related Workmentioning

confidence: 98%

“…Specifically focusing on the Raspberry Pi3 generation of single board computers, the work of Paniego et al in [17] considers architectural level performance counters in an attempt to identify a minimal set of counters that together explain a maximal proportion of the variance associated with power consumption on the Raspberry Pi3. Their models are linear regression-based with performance counter independent variables such as processor cycles, instructions completed, completed load/store instructions, branch prediction/misprediction, cache miss rates, and hardware interrupts, for example.…”

Section: Related Workmentioning

confidence: 99%

“…No detailed descriptive overview of power consumption distribution characteristics are provided, and in respect to regression model parameter estimates, no detailed overview of standard errors of regression coefficients and their significance can be found. Interestingly though, the work of Paniego et al in both [17,23] suggest that architectural level performance counters, and the degree of inherent parallelism, are parameters that account for a proportion of the variation in power consumption levels of applications running on Raspberry Pi generation boards.…”

Section: Related Workmentioning

confidence: 99%

“…More recently; Paniego et al in [24] build on their previous work communicated within [17,23], and consider alternative models to the typical multiple linear regression analysis. Their addition includes machine learning techniques such as support vector regression, Gaussian regression, and kernel ridge regression to build power consumption models.…”

Section: Related Workmentioning

confidence: 99%

See 3 more Smart Citations

Power Consumption Profiling of a Lightweight Development Board: Sensing with the INA219 and Teensy 4.0 Microcontroller

2021

View full text Add to dashboard Cite

At the heart of most technological advancements is a network of processors executing code and consuming energy. Understanding those systems’ energy consumption profiles provides optimisation possibilities and thus contributes to strategies for reducing energy consumption in general. This paper assesses the power consumption characteristics of a highly competitive low cost, small form factor development board (the Raspberry Pi4 model B), powered with the minimal load associated with its bare-metal configuration and the related impact on baseline power consumption. We also consider the load associated with an out-of-box operating system, running at several underclocking frequency scaling levels and the associated impact on baseline power consumption. Our experimental set-up consists of integrating an INA219 high-side current sense amplifier for the capturing of power, current, and voltage measurements; and a Teensy 4.0 microcontroller for sampling. Overall, our results indicate statistically significant differences in overall power consumption distribution characteristics across all models. Our results also indicate the presence of three distinct power phase envelopes and statistically significant differences in mean and median power measurements between the different underclocking frequency test cases and the bare-metal cases. Our results also indicate that power consumption is an increasing monotonic function across test scenarios. Finally, our results have also shown that isolating power consumption composite distributions increases model predictability from 67% to 97%.

show abstract

Section: Related Workmentioning

confidence: 98%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

Power Consumption Profiling of a Lightweight Development Board: Sensing with the INA219 and Teensy 4.0 Microcontroller

2021

View full text Add to dashboard Cite

show abstract

“…In contrast with [14], [33] is a power estimation prediction technique based on a statistical model. Its portability is analyzed on new generations of Raspberry Pi Single Board Computer (SBC).…”

Section: Microprocessors/cisa/(mp)socmentioning

confidence: 99%

High-level power estimation techniques in embedded systems hardware: an overview

et al. 2022

View full text Add to dashboard Cite

Power optimization has become a major concern for most digital hardware designers, particularly in early design phases and especially in limited power budget systems (battery-operated hand-held devices, electro-optical pluggable modules, IoT and green energy systems, etc.). Subsequently, early power consumption estimation at design time is crucial for power optimization. The aim of this paper is to present an overview of High-Level power estimation techniques currently available along with a comprehensive comparison between different methodologies and their applications on estimated models. When high speed and high performance are key features of a specific embedded system, increase in energy consumption becomes the main hurdle to be tackled while keeping speed/performance v/s power consumption trade-off at a minimum. This paper provides designers, interested in power consumption modeling, with knowledge on how to select best power estimation techniques applied to designated target models.

show abstract

WattEdge: A Holistic Approach for Empirical Energy Measurements in Edge Computing

Aslanpour

Toosi

Gaire

et al. 2021

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Unified Power Modeling Design for Various Raspberry Pi Generations Analyzing Different Statistical Methods

Cited by 7 publications

References 13 publications

Power Consumption Profiling of a Lightweight Development Board: Sensing with the INA219 and Teensy 4.0 Microcontroller

Power Consumption Profiling of a Lightweight Development Board: Sensing with the INA219 and Teensy 4.0 Microcontroller

High-level power estimation techniques in embedded systems hardware: an overview

WattEdge: A Holistic Approach for Empirical Energy Measurements in Edge Computing

Contact Info

Product

Resources

About