Efficient Deep Structure Learning for Resource-Limited IoT Devices

Nowadays, deep neural networks (DNNs) have been rapidly deployed to realize a number of functionalifies like sensing, imaging, classification, recognition, etc. However. the computational-intensive requirement of DNNs makes it difficult to be applicable for resource-limited Internet of Things (loT) devices. In this paper, we propose a novel pruning-based paradigm that aims to reduce the computational cost of DNNs. by uncovering a more compact structure and learning the effective weights therein, on the basis of not compromising the expressive capability of DNNs. In particular, our algorithm can achieve efficient end-to-end training that transfers a redundant neural network to a compact one with a specifically targeted compression rale directly. We comprehensively evaluate our approach on various representative benchmark datasets and compared with typical advanced convolutional neural network (C:NN) architectures. The experimental results verify the superior performance and robust effectiveness of our scheme. For example, when pruning VGG on CIFAR-10, our proposed scheme is able to significantly reduce its FLOPs (floating-point operations) and number of parameters with a proportion of 76.2% and 94.1%, respectively, while still maintaining a satisfactory accuracy. To sum up, our scheme could facilitate the integration of DNNs into the common machine-learning-based loT framework, and establish distributed training of neural networks in both cloud and edge.