An Improved Power Macro-Model for Arithmetic Datapath Components

Domenik Helms, Eike Schmidt, Arne Schulz, Ansgar Stammermann, Wolfgang Nebel
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.
9 / 2002
Proceedings of PATMOS 02, Seville, Spain
Methodik und Werkzeuge für den verlustleistungsoptimiertenAlgorithmen- und Architekturentwurf eingebetteter Systeme für die Verarbeitung von Audio- und Sprachsignalen
Verlustleistungs-Optimierungen eingebetteter Systeme

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