Why to use activation units/functions (non-linearity) inside convolution neural networks (CNNs)

I have struggled in the past to really understand the real meaning behind the use of activation functions in the CNN architecture. Let's take a 3 layer neural Network architecture (below figure), w1, w2, w3, b1, b2, b3 are the weight vectors and bias vectors between the layers. Assume X [x1, x2, x3] is the input to the network. As we know the output after a layer (neuron) is multiplication between weight and "output from the last layer" and then added bias i.e (Y WX b) Let's focus on the first row (w11,b11,w21,b21 ..)of the network. Y2 holds same linear combination with the input x1, weight (newConstant1) and bias (newConstant2) i.e W*X b.