Neural methods for dynamic branch prediction 来自 国家科技图书文献中心 喜欢 0 阅读量: 125 作者:DANIEL A. JIMENEZ,CALVIN LIN 摘要: This article presents a new and highly accurate method for branch prediction. The key idea is to use one of the simplest possible neural methods, the perceptron, ...
A collection of resources regarding the interplay between differential equations, deep learning, dynamical systems, control and numerical methods. - Zymrael/awesome-neural-ode
Min Pooling: Similar to max pooling, but for the minimum value. Average Pooling: The gradient is divided equally among all neurons in the pooling region. The CAI Neural API implements these backpropagation methods in the respective Backpropagate() functions of each pooling class. Deconvolution (Up...
STATICANDDYNAMICBRANCHPREDICTIONUSINGNEURALNETWORKSMariusSBERA*,LucianN.VINTAN**,AdrianFLOREA***“S.C.ConsultensInformationstechnikS.R.L.”Sibiu,ROMANIA,E-mail:sbmarius@usa.net**“LucianBlaga”UniversityofSibiu,ComputerScienceDepartment,Sibiu,ROMANIAE-mail:vintan@jupiter.sibiu.ro,aflorea@vectra.sibiu....
We introduce 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 predicted, that separate predicted taken from predicted not taken branches. Taken together, all of these linear functions form a ...
Bearings are very important components in mechanical equipment, and detecting bearing failures helps ensure healthy operation of mechanical equipment and can prevent catastrophic accidents. Most of the well-established detection methods do not take into
It saved the path information in function calling stack for functions and dispersed the receptor index with the path information in branch prediction. It would differentiate the branch of sub-function in different function calling and could eliminate the prediction alias e......
While direct prediction of outputs is appealing in methods using VAEs or GANs, they usually predict outputs of small, fixed sizes. Therefore, another branch of deep graph generative models employs sequential decision-making procedures to overcome these limitations. You et al. identify three ...
1Branch 0Tags Code Folders and files Name Last commit message Last commit date Latest commit robertsdionne Merge pull request#1from fengbintu/master Jul 19, 2020 51b0c53·Jul 19, 2020 History 216 Commits README.md Update README.md Jan 28, 2016 ...
Explainability Methods for Graph Convolutional Neural Networks. CVPR 2019. paper Phillip E. Pope, Soheil Kolouri, Mohammad Rostami, Charles E. Martin, Heiko Hoffmann. Can GCNs Go as Deep as CNNs? ICCV 2019. paper Guohao Li, Matthias Müller, Ali Thabet, Bernard Ghanem. Weisfeiler and Leman ...