A Hardware-oriented Approach for Efficient Active Inference Computation and Deployment

Nikola Pižurica; Nikola Milović; Igor Jovančević; Conor Heins; Miguel de Prado

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arXiv · 2508.13177

A Hardware-oriented Approach for Efficient Active Inference Computation and Deployment

Nikola Pižurica·Nikola Milović·Igor Jovančević·Conor Heins·Miguel de Prado

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Abstract

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.

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Nikola Pižurica, Nikola Milović, Igor Jovančević, Conor Heins, Miguel de Prado. 2025-08-12. A Hardware-oriented Approach for Efficient Active Inference Computation and Deployment. https://arxiv.org/abs/2508.13177

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