A Hardware-oriented Approach for Efficient Active Inference Computation and Deployment
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866918126721957888 |
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| author | Pižurica, Nikola Milović, Nikola Jovančević, Igor Heins, Conor de Prado, Miguel |
| author_facet | Pižurica, Nikola Milović, Nikola Jovančević, Igor Heins, Conor de Prado, Miguel |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_13177 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Hardware-oriented Approach for Efficient Active Inference Computation and Deployment Pižurica, Nikola Milović, Nikola Jovančević, Igor Heins, Conor de Prado, Miguel Artificial Intelligence 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. |
| title | A Hardware-oriented Approach for Efficient Active Inference Computation and Deployment |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2508.13177 |