论文标题
通过Edge-Prune框架推断易于故障的协作推断
Fault-Tolerant Collaborative Inference through the Edge-PRUNE Framework
论文作者
论文摘要
协作推断已经在机器学习方面获得了重大的研究兴趣，作为分发计算负载，减少延迟以及解决通信中隐私保护的工具。最近的协作推理框架采用了动态推理方法，例如早期外观和神经网络的运行时间分配。但是，随着机器学习框架的扩展，例如在监视应用中，需要考虑与设备故障相关的容错。本文介绍了基于正式定义的计算模型建立的边缘盈余分布式计算框架，该框架为错误的耐受性协作推断提供了灵活的基础架构。这项工作的实验部分显示了通过协作推理可实现的推理时间节省的结果，呈现容错的系统拓扑并在执行时间开销方面分析其成本。
Collaborative inference has received significant research interest in machine learning as a vehicle for distributing computation load, reducing latency, as well as addressing privacy preservation in communications. Recent collaborative inference frameworks have adopted dynamic inference methodologies such as early-exit and run-time partitioning of neural networks. However, as machine learning frameworks scale in the number of inference inputs, e.g., in surveillance applications, fault tolerance related to device failure needs to be considered. This paper presents the Edge-PRUNE distributed computing framework, built on a formally defined model of computation, which provides a flexible infrastructure for fault tolerant collaborative inference. The experimental section of this work shows results on achievable inference time savings by collaborative inference, presents fault tolerant system topologies and analyzes their cost in terms of execution time overhead.
