A Hardware-oriented Approach for Efficient Active Inference Computation and Deployment
This work addresses deployment issues for AIF agents in real-time and embedded applications, representing an incremental improvement.
The paper tackled the computational and memory challenges of deploying Active Inference (AIF) in resource-constrained environments by integrating pymdp with a sparse computational graph, resulting in over 2x latency reduction and up to 35% memory savings.
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.