An Automatic Network Structure Search Via Channel Pruning for Accelerating Human Activity Inference on Mobile Devices

Thanks to their powerful feature extraction capabilities, deep convolutional neural networks havebecome increasingly popular in sensor-based human activity recognition (HAR) in recent years.However, their high computational cost has hindered their deployment in real-world applications. Thispaper proposes a novel approach to channel pruning using the artifificial bee colony (ABC) algorithm.The ABC algorithm is a metaheuristic optimization algorithm inspired by the foraging behaviorof honeybees. Unlike traditional pruning methods that prioritize channel importance, our methodseeks to identify the optimal number of channels at each layer, i.e. the optimal pruning structure.To reduce human interference, we transform the search problem of the optimal pruning structureinto an optimization problem and solve it automatically through the ABC algorithm. Our method cansignifificantly decrease the model size and computational cost without compromising performance.We on four benchmark HAR datasets showcase the effffectiveness of ABCSearch. For example, on theWISDM dataset, we achieved an 89.42% reduction in computational cost, an 88.70% reduction inmemory consumption, and a 5× acceleration on embedded platforms while maintaining comparableaccuracy