Digital Twins, which are built on complex and heterogeneous technologies, are becoming more widely used. The purpose of this research was to come up with a qualitative methodology for assessing the economic and environmental costs associated with Digital Twins.
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In this research, we proposed an AI-assisted methodology to significantly speed up power consumption modeling. Clustering techniques are used to select representative windows, automatically reducing register transfer level (RTL) traces.
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