Abstract

A variable length (32 ~ 2,048), low power, floating point fast Fourier transform (FP-FFT) processor is designed and implemented using energy-efficient butterfly elements. The butterfly elements are implemented using distributed arithmetic (DA) algorithm that eliminates the power-consuming complex multipliers. The FFT computations are scheduled in a quasi-parallel mode with an array of 16 butterflies. The nodes of the data flow graph (DFG) of the FFT are folded to these 16 butterflies for any value of N by the control unit. Register minimization is also applied after folding to decrease the number of scratch pad registers to (log 2 N − 1) × 16. The real and imaginary parts of the samples are represented by 32-bit single-precision floating point notation to achieve high precision in the results. Thus, each sample is represented using 64 bits. Twiddle factor ROM size is reduced by 25% using the symmetry of the twiddle factors. Reconfigurability based on the sample size is achieved by the control unit. This distributed floating point arithmetic (DFPA)-based design of FFT processor implemented in 45-nm process occupies an area of 0.973 mm2 and dissipates a power of 68 mW at an operating frequency of 100 MHz. When compared with FFT processor designed in the same technology with multiplier-based butterflies, this design shows 33% less area and 38% less power. The throughput for 2,048-point FFT is 222 KS/s and the energy spent per FFT is 7.4 to 14 nJ for 64 to 2,048 points being one among the most energy-efficient FFT processors.

Highlights

  • Fast Fourier transforms (FFTs) efficiently compute the coefficients of a discrete Fourier series (DFS)

  • 1.1 Need for reconfigurable FFT In a multi-mode, multi-band, multi-functional wireless communication system like software-defined radio (SDR), orthogonal frequency division multiplexing (OFDM) is used for base band processing

  • The synthesis results of the distributed arithmetic algorithm (DAA)-based butterfly/conventional butterfly and the results of the DFPA algorithm-based butterfly (DFPABF)-based reconfigurable (64 to 2 K points) FFT processor and the conventional butterfly-based (64 to 2 K points) FFT processor with the same architecture are compared in Table 5 which shows 33% less area and 38% less power for the same architecture

Read more

Summary

Introduction

Fast Fourier transforms (FFTs) efficiently compute the coefficients of a discrete Fourier series (DFS). As the technology node shrinks, with millions of switching transistors per μm, the total power dissipated by the high performing VLSI circuits greatly increases the temperature of devices and reduces its reliability It needs higher efforts for cooling and increases the battery weight. Fast Fourier transforms (FFTs) compute the DFT efficiently with reduced number of multiplications and additions. The techniques used in developing the FFT algorithm are breaking down the DFT of a long sequence into small DFTs and exploiting the following properties of the twiddle factor Those two properties are given in Equations 3 and 4. Radix-2 DIF FFT algorithm is applied in this work

FFT processor architectures
Findings
Conclusions
Full Text
Paper version not known

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call

Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.