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

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

“…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.…”

“…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.…”

“…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.…”

“…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.…”

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

“…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).…”