DocumentCode :
3514637
Title :
Dynamic branch prediction using neural networks
Author :
Steven, Gordon ; Anguera, Rubén ; Egan, Colin ; Steven, Fleur ; Vintan, Lucian
Author_Institution :
Hertfordshire Univ., Hatfield, UK
fYear :
2001
fDate :
2001
Firstpage :
178
Lastpage :
185
Abstract :
Dynamic branch prediction in high-performance processors is a specific instance of a general time series prediction problem that occurs in many areas of science. In contrast, most branch prediction research focuses on two-level adaptive branch prediction techniques, a very specific solution to the branch prediction problem. An alternative approach is to look to other application areas and fields for novel solutions to the problem. In this paper, we examine the application of neural networks to dynamic branch prediction. Two neural networks are considered: a lecturing vector quantisation (LVQ) Network and a backpropagation network. We demonstrate that a neural predictor can achieve misprediction rates comparable to conventional two-level adaptive predictors and suggest that neural predictors merit further investigation
Keywords :
backpropagation; neural nets; parallel architectures; program compilers; time series; vector quantisation; backpropagation network; dynamic branch prediction; general time series prediction problem; high-performance processors; lecturing vector quantisation; neural networks; neural predictor; two-level adaptive predictors; Accuracy; Backpropagation; Counting circuits; History; Neural networks; Performance loss; Pipelines; Silicon; Vector quantization;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Digital Systems Design, 2001. Proceedings. Euromicro Symposium on
Conference_Location :
Warsaw
Print_ISBN :
0-7695-1239-9
Type :
conf
DOI :
10.1109/DSD.2001.952279
Filename :
952279
Link To Document :
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