The weakening of branch predictor performance as an inevitable side effect of exploiting control independence

Many algorithms are inherently sequential and hard to explicitly parallelize. Cores designed to aggressively handle these problems exhibit deeper pipelines and wider fetch widths to exploit instruction-level parallelism via out-of-order execution. As these parameters increase, so does the amount of instructions fetched along an incorrect path when a branch is mispredicted. Many of the instructions squashed after a branch are control independent, meaning they will be fetched regardless of whether the candidate branch is taken or not. There has been much research in retaining these control independent instructions on misprediction of the candidate branch. This research shows that there is potential for exploiting control independence since under favorable circumstances many benchmarks can exhibit 30% or more speedup. Though these control independent processors are meant to lessen the damage of misprediction, an inherent side-effect of fetching out of order, branch weakening, keeps realized speedup from reaching its potential. This thesis introduces, formally defines, and identifies the types of branch weakening. Useful information is provided to develop techniques that may reduce weakening. A classification is provided that measures each type of weakening to help better determine potential speedup of control independence processors. Experimentation shows that certain applications suffer greatly from weakening. Total branch mispredictions increase by 30% in several cases. Analysis has revealed two broad causes of weakening: changes in branch predictor update times and changes in the outcome history used by branch predictors. Each of these broad causes are classified into more specific causes, one of which is due to the loss of nearby correlation data and cannot be avoided. The classification technique presented in this study measures that 45% of the weakening in the selected SPEC CPU 2000 benchmarks are of this type while 40% involve other changes in outcome history. The remaining 15% is caused by changes in predictor update times. In applying fundamental techniques that reduce weakening, the Control Independence Aware Branch Predictor is developed. This predictor reduces weakening for the majority of chosen benchmarks. In doing so, a control independence processor, snipper, to attain significantly higher speedup for 10 out of 15 studied benchmarks

A case for (partially) tagged geometric history length branch prediction

International audienceIt is now widely admitted that in order to provide state-of-the-art accuracy, a conditional branch predictor must combine several predictions. Recent research has shown that an adder tree is a very effective approach for the prediction combination function. In this paper, we present a more cost effective solution for this prediction combination function for predictors relying on several predictor components indexed with different history lengths. Using geometric history length as the O-GEHL predictor, the TAGE predictor uses (partially) tagged components as the PPM-like predictor. TAGE relies on (partial) hit-miss detection as the prediction computation function. TAGE provides state-of-the-art prediction accuracy on conditional branches. In particular, at equivalent storage budgets, the TAGE predictor significantly outperforms all the predictors that were presented at the Championship Branch Prediction in december 2004. The accuracy of the prediction of the targets of indirect branches is a major issue on some applications. We show that the principles of the TAGE predictor can be directly applied to the prediction of indirect branches. The ITTAGE predictor (Indirect Target TAgged GEometric history length) significantly outperforms previous state-of-the-art indirect target branch predictors. Both TAGE and ITTAGE predictors feature tagged predictor components indexed with distinct history lengths forming a geometric series. They can be associated in a single cost-effective predictor, sharing tables and predictor logic, the COTTAGE predictor (COnditional and indirect Target TAgged GEometric history length)

BRAT: Branch Prediction Via Adaptive Training

In this thesis, BRAT is researched as a new hardware structure for cost-efficient branch prediction. Relying on the fundamentals of machine learning, BRAT computes a branch decision through a multi-layer neural network. To demonstrate the merits of BRAT, it is used to predict branches in a typical pipeline and evaluate its accuracy. By utilizing a hidden layer and activation functions, BRAT is able to introduce non-linearity and enable more accurate prediction of branch outcomes because this structure exposes relationships that may not be easily captured by a perceptron based approach or other popular methods. The memory utilized by BRAT scales linearly with the number of inputs in the decision process. At most memory footprints, BRAT is competitive with state-of-the-art branch predictors of equivalent memory budgets. Additionally, as the memory footprint is increased, it is shown how BRAT scales and how larger predictors in the future may perform.M.S

Data Collection and Machine Learning Methods for Automated Pedestrian Facility Detection and Mensuration

Large-scale collection of pedestrian facility (crosswalks, sidewalks, etc.) presence data is vital to the success of efforts to improve pedestrian facility management, safety analysis, and road network planning. However, this kind of data is typically not available on a large scale due to the high labor and time costs that are the result of relying on manual data collection methods. Therefore, methods for automating this process using techniques such as machine learning are currently being explored by researchers. In our work, we mainly focus on machine learning methods for the detection of crosswalks and sidewalks from both aerial and street-view imagery. We test data from these two viewpoints individually and with an ensemble method that we refer to as our “dual-perspective prediction model”. In order to obtain this data, we developed a data collection pipeline that combines crowdsourced pedestrian facility location data with aerial and street-view imagery from Bing Maps. In addition to the Convolutional Neural Network used to perform pedestrian facility detection using this data, we also trained a segmentation network to measure the length and width of crosswalks from aerial images. In our tests with a dual-perspective image dataset that was heavily occluded in the aerial view but relatively clear in the street view, our dual-perspective prediction model was able to increase prediction accuracy, recall, and precision by 49%, 383%, and 15%, respectively (compared to using a single perspective model based on only aerial view images). In our tests with satellite imagery provided by the Mississippi Department of Transportation, we were able to achieve accuracies as high as 99.23%, 91.26%, and 93.7% for aerial crosswalk detection, aerial sidewalk detection, and aerial crosswalk mensuration, respectively. The final system that we developed packages all of our machine learning models into an easy-to-use system that enables users to process large batches of imagery or examine individual images in a directory using a graphical interface. Our data collection and filtering guidelines can also be used to guide future research in this area by establishing standards for data quality and labelling

New ideas and trends in deep multimodal content understanding: a review

The focus of this survey is on the analysis of two modalities of multimodal deep learning: image and text. Unlike classic reviews of deep learning where monomodal image classifiers such as VGG, ResNet and Inception module are central topics, this paper will examine recent multimodal deep models and structures, including auto-encoders, generative adversarial nets and their variants. These models go beyond the simple image classifiers in which they can do uni-directional (e.g. image captioning, image generation) and bi-directional (e.g. cross-modal retrieval, visual question answering) multimodal tasks. Besides, we analyze two aspects of the challenge in terms of better content understanding in deep multimodal applications. We then introduce current ideas and trends in deep multimodal feature learning, such as feature embedding approaches and objective function design, which are crucial in overcoming the aforementioned challenges. Finally, we include several promising directions for future research.Computer Systems, Imagery and Medi