Artificial Intelligence-Driven Network-on-Chip Design Space Exploration: Neural Network Architectures for Design

Network-on-Chip (NoC) design requires exploring a high-dimensional configuration space to satisfy stringent throughput requirements and latency this http URL design space exploration techniques are often slow and struggle to handle complex, non-linear parameter this http URL work presents a machine learning-driven framework that automates NoC design space exploration using BookSim simulations and reverse neural network this http URL, we compare three architectures - a Multi-Layer Perceptron (MLP),a Conditional Diffusion Model, and a Conditional Variational Autoencoder (CVAE) to predict optimal NoC parameters given target performance this http URL pipeline generates over 150,000 simulation data points across varied mesh this http URL Conditional Diffusion Model achieved the highest predictive accuracy, attaining a mean squared error (MSE) of 0.463 on unseen this http URL, the proposed framework reduces design exploration time by several orders of magnitude, making it a practical solution for rapid and scalable NoC co-design.