  Convolution neural networks (CNNs) have shown great success in various
applications. However, the computational complexity and memory storage of CNNs
is a bottleneck for their deployment on resource-constrained devices. Recent
efforts towards reducing the computation cost and the memory overhead of CNNs
involve similarity-based passive filter pruning methods. Similarity-based
passive filter pruning methods compute a pairwise similarity matrix for the
filters and eliminate a few similar filters to obtain a small pruned CNN.
However, the computational complexity of computing the pairwise similarity
matrix is high, particularly when a convolutional layer has many filters. To
reduce the computational complexity in obtaining the pairwise similarity
matrix, we propose to use an efficient method where the complete pairwise
similarity matrix is approximated from only a few of its columns by using a
Nystr\"om approximation method. The proposed efficient similarity-based passive
filter pruning method is 3 times faster and gives same accuracy at the same
reduction in computations for CNNs compared to that of the similarity-based
pruning method that computes a complete pairwise similarity matrix. Apart from
this, the proposed efficient similarity-based pruning method performs similarly
or better than the existing norm-based pruning methods. The efficacy of the
proposed pruning method is evaluated on CNNs such as DCASE 2021 Task 1A
baseline network and a VGGish network designed for acoustic scene
classification.

Computer Vision and Pattern Recognition;Artificial Intelligence;Machine Learning;Sound;Audio and Speech Processing