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Power dissipation has become one of the key optimization conditions in logic design on field programmable gate arrays (FPGAs), thus the power estimation is necessary for logic design optimization. Nowadays, signal activity data created by logic simulation based on test vectors is essential to be used to determine the toggle rate of each signals and blocks in power estimation tools provided by field programmable gate array (FPGA) electronic design automation (EDA) tools. The accuracy of power estimation highly depends on the quality of test vectors, especially, pattern coverage. As probability distribution can describe the uncertainty signals, this work provides an algorithm which can estimate FPGAs power more effectively and accurately by using signal probability distribution rather than test vectors.
Power dissipation has become one of the key optimization conditions in logic design on field programmable gate arrays (FPGAs), thus the power estimation is necessary for logic design optimization. Nowadays, signal activity data created by logic simulation based on test vectors is essential to be used to determine the toggle rate of each signals and blocks in power estimation tools provided by field programmable gate array (FPGA) electronic design automation (EDA) tools. The accuracy of power estimation highly depends on the quality of test vectors, especially, pattern coverage As probability distribution can describe the uncertainty signals, this work provides an algorithm which can estimate FPGAs power more effectively and accurately by using signal probability distribution rather than test vectors.