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Artificial Intelligence for Iteration Count Prediction in Real-Time CORDIC Processing

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Publicat a:Mathematics vol. 13, no. 24 (2025), p. 3957-3975
Autor principal: Ratheesh, Sudheerbabu
Altres autors: Chandrika, Reghunath Lekshmi, Franzoni Valentina, Milani, Alfredo, Randieri Cristian
Publicat:
MDPI AG
Matèries:
Machine learning
Accuracy
Embedded systems
Coordinate transformations
Artificial intelligence
Digital signal processing
Support vector machines
Regression models
Neural networks
Signal processing
Mathematical functions
Medical equipment
Design
Energy efficiency
Algorithms
Computer graphics
Digital signal processors
Real time
Robotics
Accés en línia:Citation/Abstract
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Resum:The first research attempt to dynamically optimize the CORDIC algorithm’s iteration count using artificial intelligence is presented in this paper. Conventional approaches depend on a certain number of iterations, which frequently results in extra calculations and longer processing times. Our method drastically reduces the number of iterations without compromising accuracy by using machine learning regression models to predict the near-best iteration value for a given input angle. Overall efficiency is increased as a result of reduced computational complexity along with faster execution. We optimized the hyperparameters of several models, including Random Forest, XGBoost, and Support Vector Machine (SVM) Regressor, using Grid Search and Cross-Validation. Experimental results show that the SVM Regressor performs best, with a mean absolute error of 0.045 and an R2 score of 0.998. This AI-driven dynamic iteration prediction thus offers a promising route for efficient and adaptable CORDIC implementations in real-time digital signal processing applications.
ISSN:2227-7390
DOI:10.3390/math13243957
Font:Engineering Database