{"ID":2883173,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.13177","arxiv_id":"2508.13177","title":"A Hardware-oriented Approach for Efficient Active Inference Computation and Deployment","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.","short_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 unif...","url_abs":"https://arxiv.org/abs/2508.13177","url_pdf":"https://arxiv.org/pdf/2508.13177v1","authors":"[\"Nikola Pižurica\",\"Nikola Milović\",\"Igor Jovančević\",\"Conor Heins\",\"Miguel de Prado\"]","published":"2025-08-12T09:39:46Z","proceeding":"cs.AI","tasks":"[\"cs.AI\"]","methods":"[]","has_code":false}