资源受限边缘设备中分层推理的改进决策模块选择
Improved Decision Module Selection for Hierarchical Inference in Resource-Constrained Edge Devices
AI总结：
针对资源受限边缘设备的分层推理，本文提出并比较三种HI方法，以进一步优化推理任务执行并用于图像分类。
摘要：
分层推理（Hierarchical Inference，HI）范式采用分层处理：简单数据样本的推理在终端设备上被接受，而复杂数据样本则被卸载到中央服务器。HI最近已成为平衡推理准确率、数据处理、传输吞吐量和卸载成本的有效方法。该方法在涉及资源受限边缘设备的场景中被证明特别高效，例如负责执行tinyML推理的IoT传感器和微控制器单元（MCU）。值得注意的是，它优于本地推理执行、将推理卸载到边缘服务器或云设施，以及分割推理（即在两个端点之间分布式执行推理）等策略。在HI范式的基础上，本工作探索了旨在进一步优化推理任务执行的不同技术。我们提出并讨论了三种不同的HI方法，并评估了它们在图像分类中的效用。
英文摘要：
The Hierarchical Inference (HI) paradigm employs a tiered processing: the inference from simple data samples are accepted at the end device, while complex data samples are offloaded to the central servers. HI has recently emerged as an effective method for balancing inference accuracy, data processing, transmission throughput, and offloading cost. This approach proves particularly efficient in scenarios involving resource-constrained edge devices, such as IoT sensors and micro controller units (MCUs), tasked with executing tinyML inference. Notably, it outperforms strategies such as local inference execution, inference offloading to edge servers or cloud facilities, and split inference (i.e., inference execution distributed between two endpoints). Building upon the HI paradigm, this work explores different techniques aimed at further optimizing inference task execution. We propose and discuss three distinct HI approaches and evaluate their utility for image classification.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
