A Computationally Light Pruning Strategy for Single Layer Neural Networks based on Threshold Function Embedded machine learning relies on inference functions that can fit resourceconstrained, lowpower computing devices. The literature proves that single layer neural networks using threshold functions can provide a suitable trade off between classification accuracy and computational cost. In this regard, the number of neurons directly impacts both on computational complexity and on resources allocation. Thus, the present research aims at designing an efficient pruning technique that can take into account the peculiarities of the threshold function. The paper shows that feature selection criteria based on filter models can effectively be applied to neuron selection. In particular, valuable outcomes can be obtained by designing adhoc objective functions for the selection process. An extensive experimental campaign confirms that the proposed objective function compares favourably with stateoftheart pruning techniques.

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.