You will be shown an excerpt from a computer science scientific research paper, followed by a question about the paper. Please answer the question. Do not include any text in your response other than the answer.

Context: Although the base MobileNet architecture is already small and low latency, many times a specific use case or application may require the model to be smaller and faster. In order to construct these smaller and less computationally expensive models we introduce a very simple parameter \alpha called width multiplier. The role of the width multiplier \alpha is to thin a network uniformly at each layer. For a given layer and width multiplier \alpha, the number of input channels M becomes \alpha M and the number of output channels N becomes \alpha N. The computational cost of a depthwise separable convolution with width multiplier \alpha is:D_{K}\cdot D_{K}\cdot\alpha M\cdot D_{F}\cdot D_{F}+\alpha M\cdot\alpha N\cdot D_{F}\cdot D_{F}(6)where \alpha\in(0,1] with typical settings of 1, 0.75, 0.5 and 0.25. \alpha=1 is the baseline MobileNet and \alpha<1are reduced MobileNets. Width multiplier has the effect of reducing computational cost and the number of parameters quadratically by roughly \alpha^{2}. Width multiplier can be applied to any model structure to define a new smaller model with a reasonable accuracy, latency and size trade off. It is used to define a new reduced structure that needs to be trained from scratch.

Question: What is the goal of using width multiplier and how it is used ?
Width multiplier reduces computational cost and parameters by defining a new,untrained & reduced structure.