Abstract
Power dissipation due to value prediction is being more studied recently. In this paper, a new cost effective data value predictor based on a linear function is introduced. Without the complex two-level structure, the new predictor can still make correct predictions on some patterns that can only be done by the context-based data value predictors. Simulation results show that the new predictor works well with most value predictable instructions. Energy and performance impacts of storing partial tag and common sub-data values in the value predictor are studied. The two methods are found to be good ways to build better cost-performance value predictors. With about 5K bytes, the new data value predictor can obtain 16.5% maximal while 4.6% average performance improvements with the SPEC INT2000 benchmarks.
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Xiao, Y., Zhou, X., Deng, K. (2005). Making Power-Efficient Data Value Predictions. In: Srikanthan, T., Xue, J., Chang, CH. (eds) Advances in Computer Systems Architecture. ACSAC 2005. Lecture Notes in Computer Science, vol 3740. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11572961_25
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DOI: https://doi.org/10.1007/11572961_25
Publisher Name: Springer, Berlin, Heidelberg
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