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: \bulletSo that the output activations from 1x1 and 3x3 filters have the same height and width, we add a 1-pixel border of zero-padding in the input data to 3x3 filters of expand modules.\bulletReLU Nair & Hinton (2010) is applied to activations from squeeze and expand layers.\bulletDropout Srivastava et al. (2014) with a ratio of 50% is applied after the fire9 module.\bulletNote the lack of fully-connected layers in SqueezeNet; this design choice was inspired by the NiN Lin et al. (2013) architecture.\bulletWhen training SqueezeNet, we begin with a learning rate of 0.04, and we linearly decrease the learning rate throughout training, as described in Mishkin et al. (2016).For details on the training protocol (e.g. batch size, learning rate, parameter initialization), please refer to our Caffe-compatible configuration files located here: https://github.com/DeepScale/SqueezeNet.\bulletThe Caffe framework does not natively support a convolution layer that contains multiple filter resolutions (e.g. 1x1 and 3x3) Jia et al. (2014). To get around this, we implement our expand layer with two separate convolution layers: a layer with 1x1 filters, and a layer with 3x3 filters. Then, we concatenate the outputs of these layers together in the channel dimension. This is numerically equivalent to implementing one layer that contains both 1x1 and 3x3 filters.

Question: The Caffe framework does not natively support a convolution layer that contains multiple filter resolutions .To get around this, the authors implement the expand layer with two separate convolution layers. What is the additional cost incurred by using two convolution layers?
The additional cost of using 2 convolutional layers may be that the parameters of the 2 layers are now trained separately; they are not benefiting from each other being jointly optimized to perform some task and share useful information between each other while training, but output shape is still not affected by the separation i.e.,this is numerically equivalent to have one layer that contains both 1x1 and 3x3 filters.