IMPLEMENTATION OF THE BRANCH PREDICTION SCHEMES FOR THE MICROPROCESSOR OF RISC-V ARCHITECTURE

2018 ◽  
pp. 49-55
Author(s):  
E. M. Abramov
Keyword(s):  
Prediction Accuracy ◽  
Branch Prediction ◽  
Limiting Factors ◽  
Prediction Methods ◽  
Hardware Cost ◽  
Control Transfer ◽  
Prediction Module ◽  
Pipeline Processing ◽  
Instruction Pipeline

One of the limiting factors for increasing the performance of CPU computation pipeline is the pipelining of control transfer instructions. This article provides a review of the problems of raising the instruction pipeline efficiency while executing the branch instructions, by the example of microarchitecture with the implementation of open RISC-V ISA. It gives a description of the various methods of resolving the control hazards. Implementations of the various static and dynamic branch prediction methods, as well as the scheme of calculating a jump address, has been provided. For the dynamic schemes this article gives an estimate of the dependency of prediction accuracy from the size of the branch history tables. Also, it contains the results of synthesis, which allow to estimate the hardware cost of the implementation of given schemes. It has been discovered that the presence of dynamic branch prediction module at the computation pipeline is helping to raise the efficiency of pipeline processing.

scholarly journals PSXII-22 Genomic prediction accuracy for feed efficiency related traits using different pseudo-phenotypes, prediction and validation methods in Nellore cattle

2020 ◽  
Vol 98 (Supplement_4) ◽  
pp. 245-246
Author(s):  
Cláudio U Magnabosco ◽  
Fernando Lopes ◽  
Valentina Magnabosco ◽  
Raysildo Lobo ◽  
Leticia Pereira ◽  
...  
Keyword(s):  
Body Weight ◽  
Weight Gain ◽  
Genomic Prediction ◽  
Feed Efficiency ◽  
Prediction Accuracy ◽  
Body Weight Gain ◽  
Prediction Methods ◽  
Genomic Breeding ◽  
Validation Population ◽  
Nellore Cattle

Abstract The aim of the study was to evaluate prediction methods, validation approaches and pseudo-phenotypes for the prediction of the genomic breeding values of feed efficiency related traits in Nellore cattle. It used the phenotypic and genotypic information of 4,329 and 3,594 animals, respectively, which were tested for residual feed intake (RFI), dry matter intake (DMI), feed efficiency (FE), feed conversion ratio (FCR), residual body weight gain (RG), and residual intake and body weight gain (RIG). Six prediction methods were used: ssGBLUP, BayesA, BayesB, BayesCπ, BLASSO, and BayesR. Three validation approaches were used: 1) random: where the data was randomly divided into ten subsets and the validation was done in each subset at a time; 2) age: the division into the training (2010 to 2016) and validation population (2017) were based on the year of birth; 3) genetic breeding value (EBV) accuracy: the data was split in the training population being animals with accuracy above 0.45; and validation population those below 0.45. We checked the accuracy and bias of genomic value (GEBV). The results showed that the GEBV accuracy was the highest when the prediction is obtained with ssGBLUP (0.05 to 0.31) (Figure 1). The low heritability obtained, mainly for FE (0.07 ± 0.03) and FCR (0.09 ± 0.03), limited the GEBVs accuracy, which ranged from low to moderate. The regression coefficient estimates were close to 1, and similar between the prediction methods, validation approaches, and pseudo-phenotypes. The cross-validation presented the most accurate predictions ranging from 0.07 to 0.037. The prediction accuracy was higher for phenotype adjusted for fixed effects than for EBV and EBV deregressed (30.0 and 34.3%, respectively). Genomic prediction can provide a reliable estimate of genomic breeding values for RFI, DMI, RG and RGI, as to even say that those traits may have higher genetic gain than FE and FCR.

Similarity indices based on link weight assignment for link prediction of unweighted complex networks

2017 ◽  
Vol 31 (02) ◽  
pp. 1650254 ◽  
Author(s):  
Shuxin Liu ◽  
Xinsheng Ji ◽  
Caixia Liu ◽  
Yi Bai
Keyword(s):  
Complex Networks ◽  
Link Prediction ◽  
Prediction Accuracy ◽  
Prediction Methods ◽  
Close Attention ◽  
Common Neighbor ◽  
Link Weight ◽  
Similarity Indices ◽  
Clustered Networks ◽  
Local Path

Many link prediction methods have been proposed for predicting the likelihood that a link exists between two nodes in complex networks. Among these methods, similarity indices are receiving close attention. Most similarity-based methods assume that the contribution of links with different topological structures is the same in the similarity calculations. This paper proposes a local weighted method, which weights the strength of connection between each pair of nodes. Based on the local weighted method, six local weighted similarity indices extended from unweighted similarity indices (including Common Neighbor (CN), Adamic-Adar (AA), Resource Allocation (RA), Salton, Jaccard and Local Path (LP) index) are proposed. Empirical study has shown that the local weighted method can significantly improve the prediction accuracy of these unweighted similarity indices and that in sparse and weakly clustered networks, the indices perform even better.

Fault Prediction Methods Study of Machinery Based on Optimized Background Value MGM(1,m) Model

2014 ◽  
Vol 599-601 ◽  
pp. 1513-1516
Author(s):  
Zhong Hu Yuan ◽  
Feng Guo ◽  
Xiao Xuan Qi
Keyword(s):  
Prediction Accuracy ◽  
Fault Prediction ◽  
Prediction Methods ◽  
Exponential Law ◽  
Background Value ◽  
Optimal Formula

After analyzing background value error of the traditional MGM(l,m) model, the paper used the functions with non-homogeneous exponential law to fit the accumulated sequences for every variable, and get the optimal formula of background value of MGM(l,m) model, which was used for establishing the model. And the optimization effect is verified by examples. The result shows that the proposed method can significantly improve the prediction accuracy of the traditional MGM(l,m) model, and the effectiveness of the proposed method is shown.

scholarly journals Hybrid prediction method of ECG signals based on VMD, PSR, and RBF neural network

2020 ◽  
Author(s):  
Fuying Huang ◽  
Tuanfa Qin ◽  
Limei Wang ◽  
Haibin Wan
Keyword(s):  
Neural Network ◽  
Phase Space ◽  
Delay Time ◽  
Prediction Accuracy ◽  
Rbf Neural Network ◽  
Prediction Method ◽  
Prediction Methods ◽  
Embedding Dimension ◽  
Ecg Signals ◽  
Ecg Data

Abstract Background: It is significant for doctors and body area networks (BANs) to predict ECG signals accurately. At present, the prediction accuracy of many existing ECG prediction methods is generally low. In order to improve the prediction accuracy of ECG signals in BANs, a hybrid prediction method of ECG signals is proposed in this paper. Methods: The proposed prediction method combines variational mode decomposition (VMD), phase space reconstruction (PSR), and a radial basis function (RBF) neural network. First, the embedding dimension and delay time of PSR are calculated according to the trained set of ECG data. Second, the ECG data are decomposed into several intrinsic mode functions (IMFs). Third, the phase space of each IMF is reconstructed according to the embedding dimension and the delay time. Fourth, an RBF neural network is established and each IMF is predicted by the network. Finally, the prediction results of all IMFs are added to realize the final prediction result. Results: To evaluate the prediction performance of the proposed method, simulation experiments are carried out on ECG data from the MIT-BIH Arrhythmia Database. The experimental results show that the prediction index RMSE (root mean square error) of the proposed method is only 10-3 magnitude and that of some traditional prediction methods is 10-2 magnitude.Conclusions: Compared with some traditional prediction methods, the proposed method improves the prediction accuracy of ECG signals obviously.

OPTS: increasing branch prediction accuracy under context switch

2002 ◽  
Vol 26 (6) ◽  
pp. 291-300 ◽  
Author(s):  
Moon-Sang Lee ◽  
Young-Jae Kang ◽  
Joon-Won Lee ◽  
Seung-Ryoul Maeng
Keyword(s):  
Prediction Accuracy ◽  
Branch Prediction ◽  
Context Switch

Prediction of Traffic Volume in Highway Tunnel Group Region Based on Grey Markov Model

2013 ◽  
Vol 712-715 ◽  
pp. 2981-2985 ◽  
Author(s):  
Wei Zhan ◽  
Qing Lu ◽  
Yue Quan Shang
Keyword(s):  
Markov Model ◽  
Early Warning ◽  
Prediction Accuracy ◽  
Development Trend ◽  
Traffic Volume ◽  
Grey Model ◽  
Prediction Methods ◽  
Highway Tunnel ◽  
The Development Trend ◽  
Highway Management

Based on the investigation and analysis of the traffic volume in highway tunnel group region, the development trend of traffic volume is analyzed by Grey model. Then the prediction accuracy is improved by Markov optimization. The method in this paper has a better prediction accuracy and practicality in a period than other common prediction methods. It can be used for the prediction analysis of traffic volume and for early warning by highway management.

Evaluation of branch-prediction methods on traces from commercial applications

1999 ◽  
Vol 43 (4) ◽  
pp. 579-593 ◽  
Author(s):  
R. B. Hilgendorf ◽  
G. J. Heim ◽  
W. Rosenstiel
Keyword(s):  
Branch Prediction ◽  
Prediction Methods ◽  
Commercial Applications
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