Class-Aware Pruning for Efficient Neural Networks
CoRR(2023)
摘要
Deep neural networks (DNNs) have demonstrated remarkable success in various
fields. However, the large number of floating-point operations (FLOPs) in DNNs
poses challenges for their deployment in resource-constrained applications,
e.g., edge devices. To address the problem, pruning has been introduced to
reduce the computational cost in executing DNNs. Previous pruning strategies
are based on weight values, gradient values and activation outputs. Different
from previous pruning solutions, in this paper, we propose a class-aware
pruning technique to compress DNNs, which provides a novel perspective to
reduce the computational cost of DNNs. In each iteration, the neural network
training is modified to facilitate the class-aware pruning. Afterwards, the
importance of filters with respect to the number of classes is evaluated. The
filters that are only important for a few number of classes are removed. The
neural network is then retrained to compensate for the incurred accuracy loss.
The pruning iterations end until no filter can be removed anymore, indicating
that the remaining filters are very important for many classes. This pruning
technique outperforms previous pruning solutions in terms of accuracy, pruning
ratio and the reduction of FLOPs. Experimental results confirm that this
class-aware pruning technique can significantly reduce the number of weights
and FLOPs, while maintaining a high inference accuracy.
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