Network quantization is an effective solution to compress deep neural networks for practical usage. Existing network quantization methods cannot sufficiently exploit the depth information to generate low-bit compressed network. In this paper, we propose two novel network quantization approaches, single-level network quantization (SLQ) for high-bit quantization and multi-level network quantization (MLQ) for extremely low-bit quantization (ternary).We are the first to consider the network quantization from both width and depth level. In the width level, parameters are divided into two parts: one for quantization and the other for re-training to eliminate the quantization loss. SLQ leverages the distribution of the parameters to improve the width level. In the depth level, we introduce incremental layer compensation to quantize layers iteratively which decreases the quantization loss in each iteration. The proposed approaches are validated with extensive experiments based on the state-of-the-art neural networks including AlexNet, VGG-16, GoogleNet and ResNet-18. Both SLQ and MLQ achieve impressive results.
Pruning is a straightforward method to compress the networks by removing the unimportant parameters or convolutional filters. @cite_7 present an effective unstructured method to prune the parameters with values under a threshold and they reduce the model size by 9 @math on AlexNet and 16 @math on VGG-16. Structured pruning can greatly reduce the computation cost. @cite_13 prune filters with small effect on the accuracy of the model and reduce the computation cost for VGG-16 by up to 34 @math @math @math 1 @math 13 $ speed up in ALexNet.
@cite_13 @cite_7
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Network pruning is an effective way to accelerate Convolutional Neural Networks (CNNs). In recent years, structured pruning methods are proposed in favor of unstructured methods as they have shown greater speedup in practical use. Existing structured methods does pruning along two main dimensions: 3D-filter wise, i.e., remove a 3D-fllter as a whole, and filter-shape wise, i.e., remove a same position from all 3D-filters. In this work, we propose a new group-wise 2D-fllter pruning approach that is orthogonal and complementary to the existing methods. The proposed approach removes a portion of 2D-fllters from each 3D-filter according to the pruning patterns learned from the data, and leads to compressed models that do not require sophisticated implementation of convolution operations. A fine-tuning process is followed to recover the accuracy. The knowledge distillation (KD) framework is explored in the fine-tuning process to improve the performance. We present our method for learning the pruning pattens as well as the fine-tuning strategy based on knowledge distillation. The proposed approach is validated on two representative CNN models — ZF and VGG16, pre-trained on ILSVRC12. Experimental results demonstrate the effectiveness of our approach. In VGG16, we get even higher accuracy after speeding-up the network by 4 times. The success of CNNs in various applications is accompanied by a significant increase in the computation and parameter storage costs. Recent efforts toward reducing these overheads involve pruning and compressing the weights of various layers without hurting original accuracy. However, magnitude-based pruning of weights reduces a significant number of parameters from the fully connected layers and may not adequately reduce the computation costs in the convolutional layers due to irregular sparsity in the pruned networks. We present an acceleration method for CNNs, where we prune filters from CNNs that are identified as having a small effect on the output accuracy. By removing whole filters in the network together with their connecting feature maps, the computation costs are reduced significantly. In contrast to pruning weights, this approach does not result in sparse connectivity patterns. Hence, it does not need the support of sparse convolution libraries and can work with existing efficient BLAS libraries for dense matrix multiplications. We show that even simple filter pruning techniques can reduce inference costs for VGG-16 by up to 34 and ResNet-110 by up to 38 on CIFAR10 while regaining close to the original accuracy by retraining the networks. The success of CNNs in various applications is accompanied by a significant increase in the computation and parameter storage costs. Recent efforts toward reducing these overheads involve pruning and compressing the weights of various layers without hurting original accuracy. However, magnitude-based pruning of weights reduces a significant number of parameters from the fully connected layers and may not adequately reduce the computation costs in the convolutional layers due to irregular sparsity in the pruned networks. We present an acceleration method for CNNs, where we prune filters from CNNs that are identified as having a small effect on the output accuracy. By removing whole filters in the network together with their connecting feature maps, the computation costs are reduced significantly. In contrast to pruning weights, this approach does not result in sparse connectivity patterns. Hence, it does not need the support of sparse convolution libraries and can work with existing efficient BLAS libraries for dense matrix multiplications. We show that even simple filter pruning techniques can reduce inference costs for VGG-16 by up to 34 and ResNet-110 by up to 38 on CIFAR10 while regaining close to the original accuracy by retraining the networks. Pruning is an effective method to address the limitation of deploying deep neural networks (DNNs) on embedded systems. Most existing methods prune weights on a given pre-trained DNN followed by a costly fine-tuning process. In this paper, we propose a new and efficient pruning algorithm which can prune the structures of filters and filter shapes effectively. This is achieved by defining filter-wise and shape-wise scaling factors to indicate those to be weakened. Then train the network from scratch and multiply weights with corresponding scaling factors. With iterative update, the weights of the selected filters and shapes are gradually weakened to zero and then pruned with little loss to the model capability. We demonstrate the effectiveness of our approach on several CNN models and datasets. For VGG-16 on CIFAR10, we achieve more than 2 x FLOPs reduction and compression with higher accuracy. And for WRN-16-4 on CIFAR100, our method exhibits more than 2 x speedup and compression with less than 1 accuracy drop.