Exploring Convolution Neural Network for Branch Prediction

Abstract

Recently, there have been significant advances in deep neural networks (DNNs) and they have shown distinctive performance in speech recognition, natural language processing, and image recognition. In this paper, we explore DNNs to push the limit for branch prediction. We treat branch prediction as a classification problem and employ both deep convolutional neural networks (CNNs), ranging from LeNet to ResNet-50, and deep belief network (DBN) for branch prediction. We compare the effectiveness of DNNs with the state-of-the-art branch predictors, including the perceptron, our prior work, Multi-poTAGE+SC, and MTAGE+SC branch predictors. The last two are the most recent winners of championship branch prediction (CBP) contests. Several interesting observations emerged from our study. First, for branch prediction, the DNNs outperform the perceptron model as high as 60-80%. Second, we analyze the impact of the depth of CNNs (i.e., number of convolutional layers and pooling layers) on the misprediction rates. The results confirm that deeper CNN structures can lead to lower misprediction rates. Third, the DBN could outperform our prior work, but not outperform the state-of-the-art TAGE-like branch predictor; the ResNet-50 could not only outperform our prior work, but also the Multi-poTAGE+SC and MTAGE+SC.

A Survey of Deep Learning Techniques for Dynamic Branch Prediction

“…7: Mini-BranchNet Network Architecture [19] Fig. 8: Deep Belief Network Architecture [21] as a solution to mitigate the vanishing gradient problem caused by backpropagation process. DBNs can be defined as a combination of probability and statistics with neural network architectures.…”

“…In 2017 Mao et al proposed the idea of using neural networks with deep belief networks for branch prediction [22] which could outperform perceptron based BPs in terms of reducing misprediction rate. Later in 2017 [21] Mao et al discuss about the possibility of using advanced deep neural networks like convolutional neural network along with deep belief network for branch prediction. Branch prediction problem is considered as a classification problem to implement different deep learning network architectures and impact of global history register (GHR), branch global address, length of program counter of the classifier on the rate of mis-speculated branch is studied .…”

Automatic Modeling Prediction Method of Nitrogen Content in Maize Leaves Based on Machine Vision and CNN

“…Recently, deep learning based on big data has provided a strong guarantee of accurate modeling prediction [21][22][23]. Deep learning algorithms, represented by CNN have attracted the extensive attention of experts in various industrial sectors and become widely applied [24][25][26][27]. In the training and processing of many data models, these methods achieve higher efficiency than traditional machine learning algorithms, such as fully connected neural networks.…”