Fast Fourier transform (FFT) is a fundamental building block for digital signal processing applications where high processing speed is crucial. Resource utilization in implementing FFT structures can be minimized by optimizing the performance of multipliers and adders used within the design. FFTs are also widely used in various machine learning algorithms. To achieve increased processor efciency and reduced resource utilization, we propose a hardware design for Radix-2, Radix-4, and Split-Radix FFT architectures that utilizes a novel parallel prefx adder. This design ofers lower power consumption, smaller chip area, and faster operation compared to existing architectures. Our performance analysis focuses on metrics such as power consumption, clock speed, and hardware complexity for Radix-2, Radix-4, and Split-Radix FFT algorithms implemented with the proposed adder. We compare these metrics using our proposed arithmetic structure against existing adder designs. The results indicate that the SplitRadix FFT architecture achieves lower power consumption and smaller chip area compared to Radix-4 and Radix-2 methods. Additionally, the Split-Radix FFT exhibits a higher clock speed. Therefore, based on these fndings, the Split-Radix algorithm appears to be a compelling choice for implementation on feld-programmable gate arrays due to its high speed and lower power consumption.
