提出硬件友好框架，让主动推理在嵌入式设备上更快更省内存。
A Hardware-oriented Approach for Efficient Active Inference Computation and Deployment
- 构建稀疏计算图，适配硬件高效执行
- 延迟降低2倍以上，内存占用减少35%
- 适合实时与嵌入式主动推理系统部署
主动推理（Active Inference, AIF）为决策提供了稳健框架，但其计算和内存需求限制了在资源受限环境中的部署。本文提出一种方法，结合pymdp的灵活性与效率，构建统一的稀疏计算图，专为硬件高效执行设计。该方法使延迟降低超过2倍，内存占用最多减少35%，显著推进了高效主动推理智能体在实时与嵌入式应用中的部署。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。