Neural network pruning is an important step in design process of efficient neural networks for edge devices with limited computational power. Pruning is a form of knowledge transfer from the weights of the original network to a smaller target subnetwork. We propose a new method for compute-constrained structured channel-wise pruning of convolutional neural networks. The method iteratively fine-tunes the network, while gradually tapering the computation resources available to the pruned network via a holonomic constraint in the method of Lagrangian multipliers framework. An explicit and adaptive automatic control over the rate of tapering is provided. The trainable parameters of our pruning method are separate from the weights of the neural network, which allows us to avoid the interference with the neural network solver (e.g. avoid the direct dependence of pruning speed on neural network learning rates). Our method combines the rigoristic' approach by the direct application of constrained optimization, avoiding the pitfalls of ADMM-based methods, like their need to define the target amount of resources for each pruning run, and direct dependence of pruning speed and priority of pruning on the relative scale of weights between layers. For VGG-16 @ ILSVRC-2012, we achieve reduction of 15.47 -> 3.87 GMAC with only 1 top-1 accuracy reduction (68.4 -> 67.4 ). For AlexNet @ ILSVRC-2012, we achieve 0.724 -> 0.411 GMAC with 1 top-1 accuracy reduction (56.8 -> 55.8 ).
Model pruning has become a useful technique that improves the computational efficiency of deep learning, making it possible to deploy solutions on resource-limited scenarios. A widely-used practice in relevant work assumes that a smaller-norm parameter or feature plays a less informative role at the inference time. In this paper, we propose a channel pruning technique for accelerating the computations of deep convolutional neural networks (CNNs), which does not critically rely on this assumption. Instead, it focuses on direct simplification of the channel-to-channel computation graph of a CNN without the need of performing a computational difficult and not always useful task of making high-dimensional tensors of CNN structured sparse. Our approach takes two stages: the first being to adopt an end-to-end stochastic training method that eventually forces the outputs of some channels being constant, and the second being to prune those constant channels from the original neural network by adjusting the biases of their impacting layers such that the resulting compact model can be quickly fine-tuned. Our approach is mathematically appealing from an optimization perspective and easy to reproduce. We experimented our approach through several image learning benchmarks and demonstrate its interesting aspects and the competitive performance.