Data-Free Network Compression via Parametric Non-Uniform Mixed Precision Quantization

Vladimir Chikin, Mikhail Antiukh; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022, pp. 450-459

Deep Neural Networks (DNNs) usually have a large number of parameters and consume a huge volume of storage space, which limits the application of DNNs on memory-constrained devices. Network quantization is an appealing way to compress DNNs. However, most of existing quantization methods require the training dataset and a fine-tuning procedure to preserve the quality of a full-precision model. These are unavailable for the confidential scenarios due to personal privacy and security problems. Focusing on this issue, we propose a novel data-free method for network compression called PNMQ, which employs the Parametric Non-uniform Mixed precision Quantization to generate a quantized network. During the compression stage, the optimal parametric non-uniform quantization grid is calculated directly for each layer to minimize the quantization error. User can directly specify the required compression ratio of a network, which is used by the PNMQ algorithm to select bitwidths of layers. This method does not require any model retraining or expensive calculations, which allows efficient implementations for network compression on edge devices. Extensive experiments have been conducted on various computer vision tasks and the results demonstrate that PNMQ achieves better performance than other state-of-the-art methods of network compression.