Efficient CNNs via Passive Filter Pruning

Convolutional neural networks (CNNs) have shown state-of-the-art performance in various applications. However, CNNs are resource-hungry due to their requirement of high computational complexity and memory storage. Recent efforts toward achieving computational and memory efficiency in CNNs involve filter pruning methods that eliminate some of the filters in CNNs based on the “importance” of the filters. The majority of existing filter pruning methods are either “active”, which use a dataset and generate feature maps to quantify filter importance, or “passive”, which compute filter importance using entry-wise norm of the filters or by measuring similarity among filters without involving data. However, the existing passive filter pruning methods eliminate relatively smaller norm filters or similar filters without considering the significance of the filters in producing the node output, resulting in degradation in the performance. To address this, we present a passive filter pruning method where the least significant filters with relatively smaller contribution in producing output are pruned away by incorporating the operator norm of the filters. The proposed pruning method results in better performance across various CNNs compared to that of the existing passive filter pruning methods. In comparison to the existing active filter pruning methods, the proposed pruning method is more efficient and achieves similar performance as well. The efficacy of the proposed pruning method is evaluated on audio scene classification and audio tagging tasks using various CNNs architecture such as VGGish, DCASE21_Net and PANNs. The proposed pruning method reduces number of computations and parameters of the unrpuned CNNs by at least 40% and 50% respectively, enhancing inference latency while maintaining similar performance as obtained using the unpruned CNNs.