Abstract
Dynamic Structured Pruning With Novel Filter Importance and Leaky Masking Based on Convolution and Batch Normalization Parameters
Highlights
D EEP Neural Networks (DNN) based on Convolutional Neural Networks (CNN) have shown state-of-the-art performance in computer vision applications, like image classification [1]–[3], object detection [4], [5], and segmentation [6], [7]
Since structured pruning is capable of practical compression of parameters and FLOPs and does not require specialized hardware and software, this paper focuses on structured pruning
Our comprehensive experiments demonstrate that the proposed CoBaL improved performance over existing filter pruning and model compression methods
Summary
D EEP Neural Networks (DNN) based on Convolutional Neural Networks (CNN) have shown state-of-the-art performance in computer vision applications, like image classification [1]–[3], object detection [4], [5], and segmentation [6], [7]. With this improved performance, increasing storage capacity and computational cost require expensive hardware resources. CNN-based algorithms require high computational and storage capacity, which requires extensive hardware resources These problems made it difficult to deploy DNNs in limited-resource environments, such as edge devices and mobile appliances. Model compression is typically studied in various methods such as pruning [8], [9], quantization [10], knowledge distillation [11], weight sharing [12], and compact modeling [13]
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