Date of Award


Degree Type


Degree Name

Master of Science in Electrical Engineering (MSEE)


Electrical, Computer, and Biomedical Engineering

First Advisor

Resit Sendag


After over two decades of extensive research on branch prediction, branch mispredictions are still an important performance and power bottleneck for today’s aggressive processors. All this research has introduced very sophisticated and accurate branch predictor designs, TAGE predictor being the current-state-of-art.

In this work, instead of directly improving on individual predictor’s accuracy, I focus on an orthogonal statistical method called bootstrap aggregating, or bagging. Bagging is used to improve overall accuracy by using an ensemble of predictors, which are trained with slightly different data sets. Each predictor (can be same or different predictors) is trained using a resampled (with replacement) training set (bootstrapping). Then, the final prediction is simply provided by weighting or majority voting (aggregating). This work shows that applying bagging improves performance more than simply increasing the size of the predictor.



To view the content in your browser, please download Adobe Reader or, alternately,
you may Download the file to your hard drive.

NOTE: The latest versions of Adobe Reader do not support viewing PDF files within Firefox on Mac OS and if you are using a modern (Intel) Mac, there is no official plugin for viewing PDF files within the browser window.