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arXiv · 1906.00399

Multi-Objective Pruning for CNNs Using Genetic Algorithm

Chuanguang Yang·Zhulin An·Chao Li·Boyu Diao·Yongjun Xu
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Abstract

In this work, we propose a heuristic genetic algorithm (GA) for pruning convolutional neural networks (CNNs) according to the multi-objective trade-off among error, computation and sparsity. In our experiments, we apply our approach to prune pre-trained LeNet across the MNIST dataset, which reduces 95.42% parameter size and achieves 16$\times$ speedups of convolutional layer computation with tiny accuracy loss by laying emphasis on sparsity and computation, respectively. Our empirical study suggests that GA is an alternative pruning approach for obtaining a competitive compression performance. Additionally, compared with state-of-the-art approaches, GA is capable of automatically pruning CNNs based on the multi-objective importance by a pre-defined fitness function.

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Chuanguang Yang, Zhulin An, Chao Li, Boyu Diao, Yongjun Xu. 2019-06-02. Multi-Objective Pruning for CNNs Using Genetic Algorithm. https://arxiv.org/abs/1906.00399

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