Multiprocessor System-on-Chip Profiling Architecture: Design and Implementation

Chen, Po-Hui; King, Chung-Ta; Chang, Yuan-Ying; Tseng, Shau-Yin

doi:10.1109/icpads.2009.118

Cited by 7 publications

(5 citation statements)

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“…On the other hand, FPGA systems require a hardware-aware profiler. Some contributions have been achieved in work [39][40][41]. The University of Toronto has implemented a snooping software profiler to count the total number of cycles during an application execution [39].…”

Section: Exploitation Of the Proposed Methodologymentioning

confidence: 99%

“…It inserts software drivers around the software function blocks to enable or disable particular counters implemented in Airwolf. For the multiprocessor systems, a multiprocessor profiling architecture is introduced for MPSoC embedded systems [41]. These FPGA-based tools have the similar functionalities as PMCs on the embedded processors or general purpose processor.…”

Section: Exploitation Of the Proposed Methodologymentioning

confidence: 99%

See 1 more Smart Citation

Energy estimation models for video decoders: reconfigurable video coding‐CAL case‐study

Ren

Juárez

Sanz

et al. 2015

IET Computers & Digital Techniques

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In this study, a platform-independent energy estimation methodology is proposed to estimate the energy consumption of reconfigurable video coding (RVC)-CAL video codec specifications. This methodology is based on the performance monitoring counters (PMCs) of embedded platforms and demonstrates its portability, simplicity and accuracy for on-line estimation. It has two off-line procedure stages: the former, which automatically identifies the most appropriate PMCs with no specific detailed knowledge of the employed platform, and the latter, which trains the model using either a linear regression or a multivariable adaptive regression splines (MARS) method. Experimenting on an RVC-CAL decoder, the proposed PMC-driven model can achieve an average estimation error <10%. In addition, the maximal model computation overhead is 4.04%. The results show that the training video sequence has significant influence on the model accuracy. An experimental metric is introduced to achieve more stable accurate models based on a combination of training sequences. Furthermore, a comparison demonstrates better predictive ability of MARS techniques in scenarios with multi-core platforms. Finally, the experimental results show a good potential of energy efficiency improvement when the estimation model is combined into the RVC framework. In two different scenarios, the battery lifetime is increased 5.16% and 20.9%, respectively.

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Section: Exploitation Of the Proposed Methodologymentioning

confidence: 99%

Section: Exploitation Of the Proposed Methodologymentioning

confidence: 99%

Energy estimation models for video decoders: reconfigurable video coding‐CAL case‐study

Ren

Juárez

Sanz

et al. 2015

IET Computers & Digital Techniques

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“…Hardware performance counters consist of two components-event detectors and event counters [18]. Users can configure performance event detectors to detect performance events as cache misses, cycles consumed, or branch mispredictions.…”

Section: Hpc Profilesmentioning

confidence: 99%

Probability Analysis to Improve the Confidence in Profiling Accuracy

Wen

Cheng

Lin

et al. 2021

Mathematical Problems in Engineering

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Performance profiling for the system is necessary and has already been widely supported by hardware performance counters (HPC). HPC is based on the registers to count the number of events in a time interval and uses system interruption to read the number from registers to a recording file. The profiled result approximates the actual running states and is not accurate since the profiling technique uses sampling to capture the states. We do not know the actual running states before, which makes the validation on profiling results complex. Jianwei YinSome experiments-based analysis compared the running results of benchmarks running on different systems to improve the confidence of the profiling technique. But they have not explained why the sampling technique can represent the actual running states. We use the probability theory to prove that the expectation value of events profiled is an unbiased estimation of the actual states, and its variance is small enough. For knowing the actual running states, we design a simulation to generate the running states and get the profiled results. We refer to the applications running on production data centers to choose the parameters for our simulation settings. Comparing the actual running states and the profiled results shows they are similar, which proves our probability analysis is correct and improves our confidence in profiling accuracy.

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“…Certain profilers are customized to profile single task (thread) applications whereas certain profilers are able to profile multi task (thread) applications [7]. Profilers customized for multitask applications, can also profile Operating System based applications.…”

Section: Classification Based On Task Supportedmentioning

confidence: 99%