Machine Learning Based Efficiency and Power Estimation of Circular Buffer

2020 ◽  
pp. 433-444
Author(s):  
Praveen Kumar Yethirajula ◽  
Trailokya Nath Sasamal ◽  
Divya Parihar
Keyword(s):  
Machine Learning ◽  
Power Estimation ◽  
Circular Buffer

A novel approach to memory power estimation using machine learning

2010 ◽  
Author(s):  
Mel Stockman ◽  
Mariette Awad ◽  
Rahul Khanna ◽  
Christian Le ◽  
Howard David ◽  
...  
Keyword(s):  
Machine Learning ◽  
Power Estimation ◽  
Novel Approach ◽  
Memory Power

scholarly journals Machine Learning Based Power Estimation for CMOS VLSI Circuits

2021 ◽  
Keyword(s):  
Machine Learning ◽  
Power Estimation ◽  
Vlsi Circuits ◽  
Cmos Vlsi

Abstract The authors have requested that this preprint be withdrawn due to a need to make corrections.

scholarly journals Machine Learning Based Power Estimation for CMOS VLSI Circuits

2021 ◽  
Author(s):  
V. Govindaraj ◽  
B. Arunadevi
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Mean Square Error ◽  
Power Estimation ◽  
Vlsi Circuits ◽  
Coefficient Of Determination ◽  
Accurate Estimation ◽  
Mean Square ◽  
Nsga Ii ◽  
Cmos Vlsi

Abstract Nowdays, machine learning (ML) algorithms are receiving massive attention in most of the engineering application since it has capability in complex systems modelling using historical data. Estimation of power for CMOS VLSI circuit using various circuit attributes is proposed using passive machine learning based technique. The proposed method uses supervised learning method which provides a fast and accurate estimation of power without affecting the accuracy of the system. Power estimation using random forest algorithm is relatively new. Accurate estimation of power of CMOS VLSI circuits is estimated by using random forest model which is optimized and tuned by using multi-objective NSGA-II algorithm. It is inferred from the experimental results testing error varies from 1.4 percent to 6.8 percent and in terms of and Mean Square Error is 1.46e-06 in random forest method when compared to BPNN. Statistical estimation like coefficient of determination (𝑅) and Root Mean Square Error (RMSE) are done and it is proven that random Forest is best choice for power estimation of CMOS VLSI circuits with high coefficient of determination of 0.99938. and low RMSE of 0.000116.

GPGPU performance and power estimation using machine learning

2015 ◽  
Author(s):  
Gene Wu ◽  
Joseph L. Greathouse ◽  
Alexander Lyashevsky ◽  
Nuwan Jayasena ◽  
Derek Chiou
Keyword(s):  
Machine Learning ◽  
Power Estimation

Mind wandering as data augmentation: How mental travel supports abstraction

2020 ◽  
Vol 43 ◽  
Author(s):  
Myrthe Faber
Keyword(s):  
Machine Learning ◽  
Data Augmentation ◽  
Mental Content ◽  
Mind Wandering ◽  
Theoretical Framework ◽  
Important Addition

Abstract Gilead et al. state that abstraction supports mental travel, and that mental travel critically relies on abstraction. I propose an important addition to this theoretical framework, namely that mental travel might also support abstraction. Specifically, I argue that spontaneous mental travel (mind wandering), much like data augmentation in machine learning, provides variability in mental content and context necessary for abstraction.

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