Traditional branch predictors exploit correlations between pattern history and branch outcome to predict branches, but there is a stronger and more natural correlation between path history and branch outcome. I exploit this correlation with piecewise linear branch prediction, an idealized branch predictor that develops a set of linear functions, one for each program path to the branch to be pre...

Branch prediction accuracy is a very important factor for superscalar processor performance. It is the ability to predict the outcome of a branch which allows the processor to effectively use a large instruction window, and extract a larger amount of ILP. The first approach to branch prediction were static predictors, which always predicted the same direction for a given branch. The use of prof...

This technical report is a preliminary investigation into the use of Support Vector Machines (SVMs) as a method of branch prediction. We present a new dynamic branch predictor based on SVMs. The SVM predictor, at the cost of a much larger hardware budget, can return a greater accuracy than current state-of-the-art predictors by exploiting its ability to learn linearly inseparable boolean functi...

In this paper, a new reversible data hiding (RDH) algorithm that is based on the concept of shifting of prediction error histograms is proposed. The algorithm extends the efficient modification of prediction errors (MPE) algorithm by incorporating two predictors and using one prediction error value for data embedding. The motivation behind using two predictors is driven by the fact that predict...

Performance prediction has the potential of ameliorate the student model of an Intelligent Tutoring System by predicting whether a student mastered or not a specific set of skills. Recently, it has been shown, by means of a simulated learning process, how performance prediction methods based on Matrix Factorization can be used for continuous score prediction and for sequencing contents through ...

The ability to predict the directions of branches, especially conditional branches, is an important problem in modern computer architecture and advanced compilers. Many static and dynamic techniques have been proposed. Today, all state-of-the-art microprocessors have some form of hardware support for dynamic branch prediction. Static techniques, on the other hand, have not been widely studied b...

This paper proposes an indirect branch predictor based on neural learning. Neural-based conditional branch predictors have been among the most accurate in the literature, so it makes sense to adapt them to the indirect branch prediction problem. However, it is not clear how to use a predictor optimized to produce a true/false output for a problem requiring the prediction of a branch target. My ...

The performance degradation caused by branch instructions in pipelined computers is well known. The degradation is even greater on computers with multiple pipelines processing a single instruction stream, such as superscalar and scalable compound instruction-set machines (SCISM). Several branch prediction schemes have been proposed that attempt to reduce this performance penalty. One of these {...

Accurate instruction fetch and branch prediction is increasingly important on today’s superscalar architectures. Fetch prediction is the process of determining the next instruction to request from the memory subsystem. Branch prediction is the process of predicting the likely out-come of branch instructions. A branch target buffer (BTB) is often used to provide target addresses for taken branch...

In this paper, we quantify the performance of a novel family of multi-stage Two-Level Adaptive Branch Predictors. In each twolevel predictor, the PHT of a conventional Two-level Adaptive Branch Predictor is replaced by a Prediction Cache. Unlike a PHT, a Prediction Cache saves only relevant branch prediction information. Furthermore, predictions are never based on uninitialised entries and inte...