Please use this identifier to cite or link to this item:
http://hdl.handle.net/10071/26548
Author(s): | Villon, L. A. Q. Susskind, Z. Bacellar, A. T. L. Miranda, I. D. S. Araújo, L. S. de. Lima, P. M. V. Breternitz Jr, M. John, L. K. França, F. M. G. Dutra, D. L. C. |
Date: | 2022 |
Title: | A WiSARD-based conditional branch predictor |
Book title/volume: | ESANN 2022 proceedings |
Pages: | 25 - 30 |
Event title: | 30th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning |
Reference: | Villon, L. A. Q., Susskind, Z., Bacellar, A. T. L., Miranda, I. D. S., Araújo, L. S. de., Lima, P. M. V., Breternitz Jr., M., John, L. K., França, F. M. G., & Dutra, D. L. C. (2022). A WiSARD-based conditional branch predictor. In ESANN 2022 proceedings (pp. 25-30). https://doi.org/10.14428/esann/2022.ES2022-65 |
ISBN: | 978287587 084-1 |
DOI (Digital Object Identifier): | 10.14428/esann/2022.ES2022-65 |
Abstract: | Conditional branch prediction is a technique used to speculatively execute instructions before knowing the direction of conditional branch statements. Perceptron-based predictors have been extensively studied, however, they need large input sizes for the data to be linearly separable. To learn nonlinear functions from the inputs, we propose a conditional branch predictor based on the WiSARD model and compare it with two state-of-the-art predictors, the TAGE-SC-L and the Multiperspective Perceptron. We show that the WiSARD-based predictor with a smaller input size outperforms the perceptron-based predictor by about 0.09% and achieves similar accuracy to that of TAGE-SC-L. |
Peerreviewed: | yes |
Access type: | Open Access |
Appears in Collections: | ISTAR-CRI - Comunicações a conferências internacionais |
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File | Size | Format | |
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conferenceobject_91147.pdf | 3,27 MB | Adobe PDF | View/Open |
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