The key components/architecture of a convolutional neural network include:

Convolutional layers: These layers convolve the input matrix and reduce the number of free parameters and allows the network to be deeper.

Pooling layers: These layers reduce the dimensions of data by combining the outputs of neuron clusters at one layer into a single neuron in the next layer.

Fully connected layers: These layers connect every neuron in one layer to every neuron in another layer. 

Receptive field: Each neuron receives input from a restricted area of the previous layer called the neuron's receptive field. In a convolutional layer, each neuron takes input from a larger area in the input than previous layers.

Weights: The vectors of weights and bias are used by the neural's function to compute an output value from input value.