Convolutional Neural Networks (CNNs) have significantly advanced image recognition and computer vision. Their growing size and complexity are driven by the need for higher accuracy and the ability to tackle more complex tasks. Larger networks can learn richer and abstract features at various levels, enabling them to recognize not only basic patterns (e.g., edges and textures), but also more complex structures like objects and faces, even in challenging conditions like varying lighting or cluttered environments. The CNNs are scalable, but bigger networks imply more memory and compute capacity. This often becomes a problem, mainly on FPGAs, which have stringent resource restrictions, and this work tries to tackle these constraints for effective implementation.