MicroNets: A multi-phase pruning pipeline to deep ensemble learning in IoT devices

Abstract

With the proliferation of Internet of Things (IoT) devices and the launch of 5G networks, there are more than 8 billion IoT devices worldwide generating sheer amounts of data causing a throughput burden on the global network. In such an environment, Deep Neural Networks (DNNs) are pushed towards the network’s edge to unleash the power of the data on edge devices. However, there are two main challenges: (1) DNNs are computationally expensive to run on resource-constrained IoT devices, and (2) IoT typically generate noisy data due to the surrounding environment causing DNN overfitting as it learns the noise along with the underlying patterns in data. To tackle this, we propose MicroNets, a multi-phase pruning framework to enable deep ensemble learning on edge devices. The experiments on Raspberry PIs show that MicroNets generates lightweight models, and utilise ensemble learning to outperform the predictability levels of a ResNet, CIFAR10CNN baseline models (up to 7%).