Convolutional Neural Networks (CNNs) have demonstrated remarkable success in various fields of computer vision and image processing. However, the computational com- plexity and resource requirements of CNNs can limit their deployment in real-time applications or on resource-constrained devices. This paper presents a comprehensive study of the Fast Fourier Transform (FFT) as a technique for accelerating CNNs, aiming to reduce computational complexity while maintaining high performance. We explore the fundamentals of FFT-based convolution, its implementation in CNNs, and its implications for network architecture design. We begin by introducing the theoretical background of the FFT and its application in convolutional operations. Next, we present a comparative analysis of the performance, compu- tational complexity, and memory requirements of traditional spatial-domain CNNs and their FFT-based counterparts. Fur- thermore, we delve into the practical aspects of implementing FFT-accelerated CNNs on different hardware platforms, such as CPUs, GPUs, and specialized accelerators. Finally, we present various applications of FFT-accelerated CNNs in various domains, highlighting the benefits and chal- lenges of adopting this technique in real-world scenarios. Our analysis demonstrates that FFT-based convolution can lead to significant speedups and resource savings in CNNs, making them more suitable for deployment in time-critical and resource- limited environments. However, certain trade-offs must be con- sidered, such as increased algorithmic complexity and potential loss of accuracy due to numerical approximations.

FFT, CNN, Convolutional Neural Networks, Fast Fourier Transform, Faster CNN