Regular artificial neural networks do not scale very well. For example, in CIFAR, a dataset that is commonly used for training computer vision models, the images are only of size 32x32 px and have 3 color channels. That means that a single fully-connected neuron in a first hidden layer of this neural network would have 32x32x3 = 3072 weights. It is still manageable. But now imagine a bigger image, for example, 300x300x3. It would have 270,000 weights (training of which demands so much computational power)!A huge neural network like that demands a lot of resources but even then remains prone to overfitting because the large number of parameters enable it to just memorize the dataset.CNNs use parameter sharing. All neurons in a particular feature map share weights which makes the whole system less computationally intense.