STM Journals Peer-reviewed research from Consortium e-Learning Network

Journal of Microcontroller Engineering and Applications

Power Estimation of MAC Block for Artix 7 FPGA using Regression Technique

  1. Priya Bamne
  2. Dr. Ravindra V. Kshirsagar

Abstract

This paper presents the power estimation approach using suitable machine learning technique. Artix-7 FPGA has been chosen as target FPGA (Field Programmable Gate Arrays) platform for understanding the methodology of power estimation. There are various approaches of power estimation for FPGAs have been given in the literature viz. probabilistic, statistical, and LUT based etc. However, this paper discussed a supervised machine learning approach namely curve fitting and regression analysis. The approach formulates the power estimation model based on the resource estimation of the given design from the commercial tool.The findings produced using the suggested models are consistent with the power levels acquired using a commercial instrument. The MAC unit's average percentage inaccuracy is merely 5.54 percent. Other classifiers might be used to further enhance the power estimation model in the future.

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