NNFacet: splitting neural network for concurrent smart sensors

• Main Authors: Chen, Jiale, Le, Duc Van, Tan, Rui, Ho, Daren
• Other Authors: School of Computer Science and Engineering
• Format: Article
• Language: English
• Published: 2023
• Subjects: Engineering::Computer science and engineering; Internet of Things; Distributed DNN Inference
• Online Access: https://hdl.handle.net/10356/171618
• Institution: Nanyang Technological University

Various deep neural networks (DNNs) including convolutional neural networks (CNNs) and recurrent neural networks (RNNs) have shown appealing performance in various classification tasks. However, due to their large sizes, a single DNN often cannot fit into the memory of resource-constrained smart IoT sensors. This paper presents a DNN splitting framework called <italic>NNFacet</italic> that aims to run a DNN-based classification task on a total of <inline-formula><tex-math notation="LaTeX">$N$</tex-math></inline-formula> concurrent battery-based sensors observing the same physical process. We begin with determining the importance of all CNN filters or RNN units in learning each class. Then, an optimization problem divides the class set into <inline-formula><tex-math notation="LaTeX">$N$</tex-math></inline-formula> subsets and assigns them to the sensors, where the important CNN filters or RNN units associated with a class subset form a small model that is deployed to a sensor. Lastly, a multilayer perceptron is trained and deployed to a cloud or edge server, which yields the final classification result based on the low-dimensional features extracted by the sensors using their small models for the same observation. We apply NNFacet to three case studies of voice sensing, vibration sensing, and visual sensing. Extensive evaluation shows that NNFacet outperforms four baseline approaches in terms of system lifetime, latency, and classification accuracy.