Optimizing VLSI Implementation: A Neural Network Approach

Authors

  • Rajeshwar.B, AnveshThatikonda

Keywords:

VLSI, ANN, MSE, MAE, optimization

Abstract

This paper presents a comprehensive study on predicting power requirements in electronic circuit design using machine learning techniques. A dataset comprising 700 entries with seven features was prepared, including the number of transistors, gate count, input/output ports, timing constraints, and area constraints. The target variable was set as power requirements. An Artificial Neural Network (ANN) model was also developed for comparison. The models were trained over 20 epochs, and the performance was evaluated using various metrics such as Mean Squared Error (MSE), Mean Absolute Error (MAE), and R-squared (R2) error. The results demonstrated the effectiveness of the models in accurately predicting power requirements, with low MSE as 0.0033151697770614384 and high R2 error as 0.99, indicating robust performance.

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References

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Published

26.03.2024

How to Cite

Rajeshwar.B.,. (2024). Optimizing VLSI Implementation: A Neural Network Approach. International Journal of Intelligent Systems and Applications in Engineering, 12(21s), 2954 – 2958. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/5947

Issue

Vol. 12 No. 21s (2024)

Section

Research Article

License

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