Modern Convolutional Neural Networks (CNNs) are known to be computationally and memory costly owing to the deep structure that is constantly growing. A reconfigurable design is crucial in tackling this difficulty since neural network requirements are always evolving. The suggested architecture is adaptable to the needs of the neural network.