LEARNED THRESHOLD PRUNING

Abstract

This paper presents a novel differentiable method for unstructured weight pruning of deep neural networks. Our learned-threshold pruning (LTP) method learns perlayer thresholds via gradient descent, unlike conventional methods where they are set as input. Making thresholds trainable also makes LTP computationally efficient, hence scalable to deeper networks. For example, it takes 30 epochs for LTP to prune ResNet50 on ImageNet by a factor of 9.1. This is in contrast to other methods that search for per-layer thresholds via a computationally intensive iterative pruning and fine-tuning process. Additionally, with a novel differentiable L 0 regularization, LTP is able to operate effectively on architectures with batch-normalization. This is important since L 1 and L 2 penalties lose their regularizing effect in networks with batch-normalization. Finally, LTP generates a trail of progressively sparser networks from which the desired pruned network can be picked based on sparsity and performance requirements. These features allow LTP to achieve competitive compression rates on ImageNet networks such as AlexNet (26.4× compression with 79.1% Top-5 accuracy) and ResNet50 (9.1× compression with 92.0% Top-5 accuracy). We also show that LTP effectively prunes modern compact architectures, such as EfficientNet, MobileNetV2 and MixNet.

1. INTRODUCTION

Deep neural networks (DNNs) have provided state-of-the-art solutions for several challenging tasks in many domains such as computer vision, natural language understanding, and speech processing. With the increasing demand for deploying DNNs on resource-constrained edge devices, it has become even more critical to reduce the memory footprint of neural networks and also to achieve power-efficient inference on these devices. Many methods in model compression Hassibi et al. (1993) 2018) rely on removing individual weights from the neural network. Although unstructured pruning methods achieve much higher weight sparsity ratio than structured pruning, unstructured is thought to be less hardware friendly because the irregular sparsity is often difficult to exploit for efficient computation Anwar et al. (2017) . However, recent advances in AI accelerator design Ignatov et al. (2018) have targeted support for highly efficient sparse matrix multiply-and-accumulate operations. Because of this, it is getting increasingly important to develop state-of-the-art algorithms for unstructured pruning. Most unstructured weight pruning methods are based on the assumption that smaller weights do not contribute as much to the model's performance. These pruning methods iteratively prune the weights that are smaller than a certain threshold and retrain the network to regain the performance lost during pruning. A key challenge in unstructured pruning is to find an optimal setting for these pruning thresholds. Merely setting the same threshold for all layers may not be appropriate because the distribution and ranges of the weights in each layer can be very different. Also, different layers may have varying sensitivities to pruning, depending on their position in the network (initial layers versus final layers) or their type (depth-wise separable versus standard convolutional layers). The



; LeCun et al. (1989); Han et al. (2015b); Zhang et al. (2018), model quantization Jacob et al. (2018); Lin et al. (2016); Zhou et al. (2017); Faraone et al. (2018) and neural architecture search Sandler et al. (2018); Tan & Le (2019a); Cai et al. (2018); Wu et al. (2019) have been introduced with these goals in mind. Neural network compression mainly falls into two categories: structured and unstructured pruning. Structured pruning methods, e.g., He et al. (2017); Li et al. (2017); Zhang et al. (2016); He et al. (2018), change the network's architecture by removing input channels from convolutional layers or by applying tensor decomposition to the layer weight matrices whereas unstructured pruning methods such as Han et al. (2015b); Frankle & Carbin (2019); Zhang et al. (

