Optimization Strategies in Quantized Neural Networks: A Review Deep neural networks(DNNs) have achieved great success in many realworld applications, but they also had some drawbacks such as considerable storage requirement, large computational power consumption and delay for training and inference, making it impracticable to deploy stateoftheart models into embedded systems and portable devices. Thus, the demand of compressing DNNs has been taken into consideration. In this paper, we focus on quantized neural networks, which is one scheme of compressing DNNs. At first we introduce some baseline works in quantized neural networks and then give a review on optimization ways used in quantizing neural networks. In our perspective, these methods fall into two categories: minimizing quantization error and minimizing loss function. Specialized introduction for each category follows after baseline works. We also make some comments to each category and some methods. Finally, we discuss on some possible directions of this area and make a conclusion.

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.