scholarly journals The YAGS branch prediction scheme

2002 ◽  
Author(s):  
A.N. Eden ◽  
T. Mudge
Keyword(s):  
Branch Prediction ◽  
Prediction Scheme

The impact of unresolved branches on branch prediction scheme performance

1994 ◽  
Vol 22 (2) ◽  
pp. 12-21 ◽  
Author(s):  
A. R. Talcott ◽  
W. Yamamoto ◽  
M. J. Serrano ◽  
R. C. Wood ◽  
M. Nemirovsky
Keyword(s):  
Branch Prediction ◽  
Prediction Scheme ◽  
The Impact

A Hybrid Branch Prediction Approach for High- Performance Processors

2021 ◽  
Vol 14 ◽  
Author(s):  
Sweety Nain ◽  
Prachi Chaudhary
Keyword(s):  
High Performance ◽  
Clock Cycle ◽  
Branch Prediction ◽  
Accuracy Rate ◽  
Local Branch ◽  
Prediction Scheme ◽  
Prediction Technique ◽  
Global And Local ◽  
Prediction Approach ◽  
Single Branch

Background: In a parallel processor, the pipeline cannot fetch the conditional instructions with the next clock cycle, leading to a pipeline stall. So, conditional instructions create a problem in the pipeline because the proper path can only be known after the branch execution. To accurately predict branches, a significant predictor is proposed for the prediction of conditional branch instruction. Method: In this paper, a single branch prediction and a correlation branch prediction scheme are applied to the different trace files by using the concept of saturating counters. Further, a hybrid branch prediction scheme is proposed, which uses both global and local branch information, providing more accuracy than the single and correlation branch prediction schemes. Results: Firstly, a single branch prediction and correlation branch prediction technique are applied to the trace files using saturating counters. By comparison, it can be observed that a correlation branch prediction technique provides better results by enhancing the accuracy rate of 2.25% than the simple branch prediction. Further, a hybrid branch prediction scheme is proposed, which uses both global and local branch information, providing more accuracy than the single and correlation branch prediction schemes. The obtained results suggest that the proposed hybrid branch prediction schemes provide an increased accuracy rate of 3.68% and 1.43% than single branch prediction and correlation branch prediction. Conclusion: The proposed hybrid branch prediction scheme gives a lower misprediction rate and higher accuracy rate than the simple branch prediction scheme and correlation branch prediction scheme.

scholarly journals Using vision-based object detection for link quality prediction in 5.6-GHz channel

2020 ◽  
Vol 2020 (1) ◽  
Author(s):  
Riichi Kudo ◽  
Kahoko Takahashi ◽  
Takeru Inoue ◽  
Kohei Mizuno
Keyword(s):  
Deep Learning ◽  
Object Detection ◽  
Wireless Lan ◽  
Autonomous Robots ◽  
Learning Technologies ◽  
Link Quality ◽  
Automated Driving ◽  
Position Information ◽  
Prediction Scheme ◽  
Accurate Perception

Abstract Various smart connected devices are emerging like automated driving cars, autonomous robots, and remote-controlled construction vehicles. These devices have vision systems to conduct their operations without collision. Machine vision technology is becoming more accessible to perceive self-position and/or the surrounding environment thanks to the great advances in deep learning technologies. The accurate perception information of these smart connected devices makes it possible to predict wireless link quality (LQ). This paper proposes an LQ prediction scheme that applies machine learning to HD camera output to forecast the influence of surrounding mobile objects on LQ. The proposed scheme utilizes object detection based on deep learning and learns the relationship between the detected object position information and the LQ. Outdoor experiments show that LQ prediction proposal can well predict the throughput for around 1 s into the future in a 5.6-GHz wireless LAN channel.

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