PRUNING DEEP NEURAL NETWORKS FROM A SPARSITY PERSPECTIVE

Abstract

In recent years, deep network pruning has attracted significant attention in order to enable the rapid deployment of AI into small devices with computation and memory constraints. Pruning is often achieved by dropping redundant weights, neurons, or layers of a deep network while attempting to retain a comparable test performance. Many deep pruning algorithms have been proposed with impressive empirical success. However, existing approaches lack a quantifiable measure to estimate the compressibility of a sub-network during each pruning iteration and thus may underprune or over-prune the model. In this work, we propose PQ Index (PQI) to measure the potential compressibility of deep neural networks and use this to develop a Sparsity-informed Adaptive Pruning (SAP) algorithm. Our extensive experiments corroborate the hypothesis that for a generic pruning procedure, PQI decreases first when a large model is being effectively regularized and then increases when its compressibility reaches a limit that appears to correspond to the beginning of underfitting. Subsequently, PQI decreases again when the model collapse and significant deterioration in the performance of the model start to occur. Additionally, our experiments demonstrate that the proposed adaptive pruning algorithm with proper choice of hyper-parameters is superior to the iterative pruning algorithms such as the lottery ticket-based pruning methods, in terms of both compression efficiency and robustness. Our code is available here.

1. INTRODUCTION

Over-parameterized deep neural networks have been applied with enormous success in a variety of fields, including computer vision (Krizhevsky et al., 2012; He et al., 2016b; Redmon et al., 2016) , natural language processing (Devlin et al., 2018; Radford et al., 2018) , audio signal processing (Oord et al., 2016; Schneider et al., 2019; Wang et al., 2020) , and distributed learning (Konečnỳ et al., 2016; Ding et al., 2022; Diao et al., 2022) . These deep neural networks have significantly expanded in size. For example, LeNet-5 (LeCun et al., 1998) (1998; image classification) has 60 thousand parameters whereas GPT-3 (Brown et al., 2020) (2020; language modeling) has 175 billion parameters. This rapid growth in size has necessitated the deployment of a vast amount of computation, storage, and energy resources. Due to hardware constraints, these enormous model sizes may be a barrier to deployment in some edge devices such as mobile phones and virtual assistants. This has greatly increased interest in deep neural network compression/pruning. To this end, various researchers have developed empirical methods of building much simpler networks with similar performance based ⇤ Equally contributed

