Lowprecision CNN Model Quantization based on Optimal Scaling Factor Estimation With the development of convolutional neural networks (CNNs), researchers have acquired satisfactory performances on computer vision tasks such as image classification and semantic segmentation. However, CNNs contain millons of weight parameters and demand huge storage and computation resources, making it hard to deploy on constrained hardwares. To compress the model size and accelerate the deployment speed, many model compression methods have been proposed, including quantization methods that aim to reduce network redundancy by decreasing the representational bits for weights (and activations). However, for lowbit quantization, the inevitable quantization error will lead to significant accuracy degradation and gradient mismatch. In this paper, we propose Scale Estimation Quantization (SEQ). To reduce the quantization error, we analyze the variance of error derived by the quantization process. By exploiting the distributions of network values, we reduce quantization error and estimate the optimal scale parameters for our proposed quantization function. Further more, to deal with gradient mismatch problem in backward propagation, we propose backward approximation. We apply our algorithm on image classification tasks. Our method achieves a close performance to fullprecision counterparts on VGGSmall and AlexNet with 1bit weights and 2bit activations.

Feature Map Alignment: Towards Efficient Design of Mixedprecision Quantization Scheme Quantization is known as an effective compression method for deploying neural networks on mobile devices. However, most existing works train from scratch a quantized network with universal bitwidth for all layers, making it hard to find the optimal tradeoff between compression ratio and inference accuracy. In this paper, we propose a novel posttraining quantization approach which derives a flexible bitwidth scheme. Our algorithm progressively downgrades bitwidth of chosen layer in the network and performs feature map alignment with pretrained model. The algorithm comprises a meter of layer sensitivity and an iterative quantizer. Specifically, the meter dynamically estimates for every layer the error of quantization on its output feature map, meanwhile the error serves as an objective function to be minimized by the quantizer. Extensive experiments on CIFAR10 and ImageNet ILSVRC2012 datasets demonstrate that the proposed approach achieves impressive results for mainstream neural networks.