Proceedings of the 6th Workshop on Rapid Simulation and Performance Evaluation: Methods and Tools 2014
DOI: 10.1145/2555486.2555491
|View full text |Cite
|
Sign up to set email alerts
|

System-level power estimation tool for embedded processor based platforms

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1
1
1
1

Citation Types

1
21
0

Year Published

2015
2015
2022
2022

Publication Types

Select...
5
2
1

Relationship

1
7

Authors

Journals

citations
Cited by 45 publications
(22 citation statements)
references
References 18 publications
1
21
0
Order By: Relevance
“…In Step 3 (described in Section VI) we describe our robust model formulation where we use our knowledge of how power is consumed in CPUs, as opposed to adding regression coefficients directly to PMC data as is typical in existing works [11], [20], [21], [24]. Our formulation reduces multicollinearity, separates dynamic and static power consumption, works with any given voltage and frequency, and, when combined with the added model stability, allows the model building experiment duration to be reduced by 100× while trading off only 0.6% error.…”
Section: Proposed Methodologymentioning
confidence: 99%
See 2 more Smart Citations
“…In Step 3 (described in Section VI) we describe our robust model formulation where we use our knowledge of how power is consumed in CPUs, as opposed to adding regression coefficients directly to PMC data as is typical in existing works [11], [20], [21], [24]. Our formulation reduces multicollinearity, separates dynamic and static power consumption, works with any given voltage and frequency, and, when combined with the added model stability, allows the model building experiment duration to be reduced by 100× while trading off only 0.6% error.…”
Section: Proposed Methodologymentioning
confidence: 99%
“…In [10] significant sources of errors are found in McPAT which are largely caused by abstraction error. Rethinagiri et al [11] show McPAT to have average power errors of over 20% for most of the tested workloads when comparing McPAT to a physical ARM Cortex-A9 device. Bottom-up power models are generally unsuitable for run-time management applications due to their relatively poor accuracy and large computational complexity.…”
Section: Related Workmentioning
confidence: 97%
See 1 more Smart Citation
“…In existing works on PMC-based run-time power models, the independent variables (i.e. PMC events and sometimes the CPU voltage (V DD ), the clock frequency (f clk and/or the temperature, T ) are inserted directly into a regression tool [9], [10], [25]- [27]; the relationships between the variables and power consumption are not considered. A recent work [27], which evaluates the state-of-the-art and typifies the general approach, proposes the model equation shown in Equation 1.…”
Section: Regression-based Modelling Methodologymentioning
confidence: 99%
“…PETS was initially developed for the evaluation of MPSoC systems [7][8][9]. In ParaDIME, we have extended it to model a variety of other systems, including GPUs [10], DSPs, FPGAs [11] and multi-core (dual-and quad-core) ARM processors [12]. Fig.…”
Section: Heterogeneous Computingmentioning
confidence: 99%