Distributed Inference with Deep Learning Models across Heterogeneous Edge Devices

Recent years witnessed an increasing research attention in deploying deep learning models on edge devices for inference. Due to limited capabilities and power constraints, it may be necessary to distribute the inference workload across multiple devices. Existing mechanisms divided the model across edge devices with the assumption that deep learning models are constructed with a chain of layers. In reality, however, modern deep learning models are more complex, involving a directed acyclic graph (DAG) rather than a chain of layers.

In this paper, we present EdgeFlow, a new distributed inference mechanism designed for general DAG structured deep learning models. Specifically, EdgeFlow partitions model layers into independent execution units with a new progressive model partitioning algorithm. By producing near-optimal model partitions, our new algorithm seeks to improve the run-time performance of distributed inference as these partitions are distributed across the edge devices. During inference, EdgeFlow orchestrates the intermediate results flowing through these units to fulfill the complicated layer dependencies. We have implemented Edge-Flow based on PyTorch, and evaluated it with state-of-theart deep learning models in different structures. The results show that EdgeFlow reducing the inference latency by up to 40.2% compared with other approaches, which demonstrates the effectiveness of our design.