Design and Implementation of Floating-Point Butterfly Architecture Based on Multi-Operand Adders

In this paper we have here in the processor FFT and FFT butterfly structure, reading, writing and execution addresses. Fast Fourier Transform (FFT) coprocessor having a noticeable impact on the performance of communication systems, has been a hot topic of research for many years. FFT function over the complex numbers in a row, also known as butterfly units have to add and multiply. , FFT architectures applicable to floating point (FP) arithmetic units have become more popular recently, especially the butterfly. FP is dismissing concerns (eg, scaling and overflow / flow) compute-intensive tasks from the generalpurpose processor offloads. However, FP butterfly, the main difficulty is slow as compared with the fixedpoint counter. To mitigate the slowdown in high-speed FP FP reveals incentive to the development of the butterfly structure. This brief productAB e ± ± CD, signedbinary digit (BSD) on the basis of representation to count, add (FDPA) unit to a faster one using drops were invented FP FP proposed butterfly unit. Three method FP FP BSD BSD adder and multiplier stable unit are part of the proposed FDPA. BSD is a carry-limited so as to improve the speed of the unit adder FDPA proposed and three horizontal BSD method adder and multiplier are used. Moreover, the change in the booth is used to speed up the encoding module is BSD. FP results in the synthesis of the proposed structure of the butterfly, but more than in previous images show that the cost is much faster. The index of signed binary digit (BSD) represented by the butterfly unit, the complex number system, fast Fourier transform (FFT), floating point (FP), the unnecessary number system, in addition to the three method. INTRODUCTION: Joseph Fourier in 1811 in a paper presented to the French Academy of Sciences. As soon as the paper is published, the Fourier effect and unbenownst, yet many problem domains, which would have a lasting impact on a range. Fourier presented by the magazine in its original domain (eg, time) equally distributed samples to the frequency domain as a method of transforming a set _nite Fourier transform concept was introduced. Apply the mathematical concept of a set of complex numbers and Fourier in_nite the transition does not involve integration. This is a continuing type of Fourier transform has many applications in physics and engineering, but it is the form of a discrete Fourier transform of the computer systems that can easily [53] can be implemented. The discrete Fourier transform (DFT) and its applications in computer science in 1965 (FFT) to transform the James Cooley and John Tukey [18] led to the fast Fourier. Divide and conquer using the method, FFT O (N2) O (Nlog (N)) also reduces the computational complexity of the DFT. When such signi_cant computational complexity, FFT Fourier transform many di_cult problems were a convenient solution. Turning around the areas of digital signal pro cessing and the number of FFT knots, which in the 20th century, [22] is considered to be one of the most inuential algorithms.