Convolutional neural networks (CNNs) are a cornerstone of modern artificial intelligence, but their immense computational requirements present significant challenges for traditional computing platforms like CPUs and GPUs. This chapter provides a holistic overview of CNNs and the specialized hardware designed to accelerate their performance. It begins with a foundational exploration of CNN principles, detailing the architecture and function of key layers, including convolutional, pooling, normalization, activation, and fully connected layers. The evolution of landmark CNN models is examined to illustrate key design innovations such as residual connections, attention mechanisms, and depthwise separable convolutions. Building on this, the chapter motivates the need for dedicated hardware by analyzing the limitations of general-purpose processors and outlining the core design challenges in hardware acceleration, such as memory bandwidth, data movement, and power efficiency. A comprehensive survey of existing hardware solutions is presented, comparing the architectures and performance trade-offs of GPUs, FPGAs, and ASICs. Finally, the chapter highlights emerging trends and future directions. This chapter serves as a comprehensive guide to the principles, design, and implementation of CNN hardware accelerators, bridging the gap between deep learning theory and practical hardware optimization.