Active Inference (AIF) offers a robust framework for decision-making, yet its computational and memory demands pose challenges for deployment, especially in resource-constrained environments. This work presents a methodology that facilitates AIF's deployment by integrating pymdp's flexibility and efficiency with a unified, sparse, computational graph tailored for hardware-efficient execution. Our approach reduces latency by over 2x and memory by up to 35%, advancing the deployment of efficient AIF agents for real-time and embedded applications.
@article{arxiv.2508.13177,
title = {A Hardware-oriented Approach for Efficient Active Inference Computation and Deployment},
author = {Nikola Pižurica and Nikola Milović and Igor Jovančević and Conor Heins and Miguel de Prado},
journal= {arXiv preprint arXiv:2508.13177},
year = {2025}
}