IONN

Abstract

Current wisdom to run computation-intensive deep neural network (DNN) on resource-constrained mobile devices is allowing the mobile clients to make DNN queries to central cloud servers, where the corresponding DNN models are pre-installed. Unfortunately, this centralized, cloud-based DNN offloading is not appropriate for emerging decentralized cloud infrastructures (e.g., cloudlet, edge/fog servers), where the client may send computation requests to any nearby server located at the edge of the network. To use such a generic edge server for DNN execution, the client should first upload its DNN model to the server, yet it can seriously delay query processing due to long uploading time. This paper proposes IONN (Incremental Offloading of Neural Network), a partitioning-based DNN offloading technique for edge computing. IONN divides a client's DNN model into a few partitions and uploads them to the edge server one by one. The server incrementally builds the DNN model as each DNN partition arrives, allowing the client to start offloading partial DNN execution even before the entire DNN model is uploaded. To decide the best DNN partitions and the uploading order, IONN uses a novel graph-based algorithm. Our experiments show that IONN significantly improves query performance in realistic hardware configurations and network conditions.