DNN inference can be accelerated by distributing the workload among a cluster of collaborative edge nodes. Heterogeneity among edge devices and accuracy-performance trade-offs of DNN models present a complex exploration space while catering to the inference performance requirements. In this work, we propose adaptive workload distribution for DNN inference, jointly considering node-level heterogeneity of edge devices, and application-specific accuracy and performance requirements. Our proposed approach combinatorially optimizes heterogeneity-aware workload partitioning and dynamic accuracy configuration of DNN models to ensure performance and accuracy guarantees. We tested our approach on an edge cluster of Odroid XU4, Raspberry Pi4, and Jetson Nano boards and achieved an average gain of 41.52% in performance and 5.2% in output accuracy as compared to state-of-the-art workload distribution strategies.

在本研究中，我们提出了一种自适应工作负载分配方法，联合考虑边缘设备的节点异构性以及应用特定的准确性和性能要求，通过组合优化异构感知的工作负载分割和动态准确配置DNN模型，以确保性能和准确度保证。我们在Odroid XU4、Raspberry Pi4和Jetson Nano板的边缘集群上进行了测试，并与最先进的工作负载分配策略相比，平均性能提升了41.52%，输出准确度提升了5.2%。

面向协作边缘平台的自适应工作负载分配用于重视准确性的深度神经网络推理