V. Govindaraj

A random forest framework for automated fault classification in analog filter circuits using simulation-derived features

The identification of faults in analog filter electronic systems has been challenging due to their non-linear characteristics, sensitivity to parameter variations and complex component interactions. Traditional fault diagnosis methods often involve lengthy testing procedures and fail to provide reliable results for mod...

Computational intelligence enabled hardware software co design for energy efficient Edge Artificial Intelligence

A structured narrative review of the latest breakthroughs achieved in the development of energy-efficient Edge AI, focusing specifically on hardware-software co-design practices aimed at improving inference quality, demonstrates that hardware-aware model optimization and heterogeneous computing significantly improve pe...

A hybrid random forest–ANFIS framework for accurate power estimation in CMOS VLSI circuits

Accurate estimation of power is a main requirement throughout the use of Complementary Metal–Oxide–Semiconductor Very Large-Scale Integration (CMOS VLSI) because it determines how efficiently energy is used; how hot a device will get from high temperatures; whether a device will work properly over long periods of time...

Reconfigurable and Scalable Digital Signal Processing Hardware for Advanced Wireless Transmitters with Improved Verification and Reliability

Computational modeling of catastrophic fault classification in Digital to Analog Converters using machine learning

This study compares the performance of Back Propagation Neural Network, Support Vector Machine, and Probabilistic Neural Network to classify catastrophic faults in both 16-bit Charge Scaling and Binary Weighted DACs that have experienced process variation such as variable threshold voltage, oxide thickness and temperat...