Hierarchical decomposition of CNN for resource-constrained mechanical vibration WSN edge computing
The microcontroller of wireless sensor network(WSN)nodes used for mechanical vibration monitoring requires intricate edge computing,yet face limitations in hardware resources.Convolutional neural network(CNN),as a high-performance and commonly used deep learning algorithm,can enhance the computational capabilities of edge WSN nodes when run on microcontroller units(MCUs).This paper proposes a hierarchical decomposition method for CNN models without modification,addressing the challenge of running non-lightweight CNN on resource-constrained MCU and enhancing the computational capabilities of mechanical vibration WSN nodes.First,a file structure is designed to decompose and store CNN model parameters.Subsequently,a memory management method is proposed,and the consumption process of random-access memory is derived.Finally,a parameter localization method is introduced to accurately and efficiently retrieve model parameters.Experiments demonstrated that with only 1.76 KB of RAM and 2.14 KB of Flash,high-precision edge computing recognition tasks can be accomplished within 3.15 ms.
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