DISNET: Distributed Micro-Split Deep Learning in Heterogeneous Dynamic IoT

The key impediments to deploying deep neural networks (DNN) in IoT edge environments lie in the gap between the expensive DNN computation and the limited computing capability of IoT devices. Current state-of-the-art machine learning models have significant demands on memory, computation, and energy and raise challenges for integrating them with the decentralized operation of heterogeneous and resource-constrained IoT devices. Recent studies have proposed the cooperative execution of DNN models in IoT devices to enhance the reliability, privacy, and efficiency of intelligent IoT systems but disregarded flexible finegrained model partitioning schemes for optimal distribution of DNN execution tasks in dynamic IoT networks. In this paper, we propose DISNET, a distributed micro-split deep learning scheme for heterogeneous dynamic IoT. DISNET accelerates inference time and minimizes energy consumption by combining vertical (layer-based) and horizontal DNN partitioning to enable flexible, distributed, and parallel execution of neural network models on heterogeneous IoT devices. DISNET considers the IoT devices’ computing and communication resources and the network conditions for resource-aware cooperative DNN Inference. Experimental evaluation in dynamic IoT networks shows that DISNET reduces the DNN inference latency and energy consumption by up to 5.2× and 6×, respectively, compared to two state-of-the-art schemes without loss of accuracy.