Model pruning techniques in the Internet of things: state of the art, methods and perspectives - ZHAO Junhui; LI Huaicheng; WANG Dongming; LI Jiamin; ZHOU Yiqing; SHU Feng (物联网学报)
In the context of the rapid development of Internet of things (IoT) technology, IoT devices faced challenges in running complex artificial intelligence (AI) algorithms, especially deep learning models, due to the limitations of computing power, storage space, communication bandwidth, and battery life. Model pruning technology could effectively reduce computation and storage requirements by reducing redundant parameters in neural networks without impairing the performance of AI models. This technique was extremely suitable for optimising AI models deployed on IoT devices. Firstly, two typical model pruning techniques-structured pruning and unstructured pruning, which were currently popular and suitable for different application scenarios, were reviewed. Secondly, the diverse applications of these methods in IoT environments were analysed in detail. Finally, the limitations of the current model pruning were discussed in detail in the light of the latest research results, and the future development direction of model pruning methods in IoT was outlooked.