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

arXiv: https://arxiv.org/abs/2512.07877 · HTML: https://mlsanity.com/paper/2512.07877/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 17 references that are in the map.
- First posted on arXiv 27 Nov 2025.

## Builds on (most cited first, 3 of 3)

- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [Pythia: A Customizable Hardware Prefetching Framework Using Online Reinforcement Learning](https://mlsanity.com/paper/2109.12021/) (2021)
- [Analytical Performance Models for NoCs with Multiple Priority Traffic Classes](https://mlsanity.com/paper/1908.02408/) (2019)

## Abstract (arXiv)

Network-on-Chip (NoC) design requires exploring a high-dimensional configuration space to satisfy stringent throughput requirements and latency constraints. Traditional design space exploration techniques are often slow and struggle to handle complex, non-linear parameter interactions. This work presents a machine learning-driven framework that automates NoC design space exploration using BookSim simulations and reverse neural network models. Specifically, 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 metrics. Our pipeline generates over 150,000 simulation data points across varied mesh topologies. The Conditional Diffusion Model achieved the highest predictive accuracy, attaining a mean squared error (MSE) of 0.463 on unseen data. Furthermore, the proposed framework reduces design exploration time by several orders of magnitude, making it a practical solution for rapid and scalable NoC co-design.