Efficient inference of Convolutional Neural Networks is a thriving topic
recently. It is desirable to achieve the maximal test accuracy under given
inference budget constraints when deploying a pre-trained model. Network
pruning is a commonly used technique while it may produce irregular sparse
models that can hardly gain actual speed-up. Group convolution is a promising
pruning target due to its regular structure; however, incorporating such
structure into the pruning procedure is challenging. It is because structural
constraints are hard to describe and can make pruning intractable to solve. The
need for configuring group convolution architecture, i.e., the number of
groups, that maximises test accuracy also increases difficulty.
This paper presents an efficient method to address this challenge. We
formulate group convolution pruning as finding the optimal channel permutation
to impose structural constraints and solve it efficiently by heuristics. We
also apply local search to exploring group configuration based on estimated
pruning cost to maximise test accuracy. Compared to prior work, results show
that our method produces competitive group convolution models for various tasks
within a shorter pruning period and enables rapid group configuration
exploration subject to inference budget constraints.

本文介绍了一种有效的方法来解决组卷积剪枝的挑战，即将其制定为寻找最优通道置换以施加结构约束并通过启发式方法高效地解决。同时，本方法还应用启发式方法来探索基于估算的剪枝成本的分组配置，以最大化测试准确性。与之前的工作相比，结果表明我们的方法在更短的剪枝时间内为各种任务生成具有竞争力的组卷积模型，并可快速探索组配置以符合推断预算约束。