We propose an improved power macro-model for arithmetic datapath components, which is based on spatio-temporal correlations of two consecutive input vectors and the output vector. Based on the enhanced Hamming-distance model [3], we introduce an additional spatial distance for the input vector and the Hamming-distance of the output vector to improve model accuracy significantly. Experimental results show that the models standard deviation is reduced by 3% for small components and up to 23% for complex components. Because of its fast and accurate power prediction, this model can be used for fast high-level power analysis.
CITATION STYLE
Helms, D., Schmidt, E., Schulz, A., Stammermann, A., & Nebel, W. (2002). An improved power macro-model for arithmetic datapath components. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 2451, pp. 16–24). Springer Verlag. https://doi.org/10.1007/3-540-45716-x_2
Mendeley helps you to discover research relevant for your work.