xilinxFFT_fftxilinx,xilinx的fft资源-CSDN文库

共255个文件

didat：91个

c：43个

obj：43个

FFT

FPGA

VHDL

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在本文中，我们将深入探讨基于Xilinx公司的快速傅里叶变换（FFT）核在FPGA实现中的应用。FFT是一种高效算法，用于计算离散傅里叶变换（DFT），广泛应用于信号处理、图像分析、通信等领域。Xilinx作为全球领先的FPGA供应商，提供了一系列的IP核，包括FFT核，使得开发者能够方便地在FPGA硬件上实现FFT功能。我们需要了解FFT的基本原理。FFT是一种将DFT分解为更小的DFT的方法，通过分治策略极大地减少了计算量。传统的DFT计算复杂度为O(N^2)，而FFT则可以降低到O(N log N)。这对于处理大量数据的实时系统来说，是至关重要的优化。 Xilinx的FFT IP核是预先设计好的硬件模块，它采用VHDL或Verilog等硬件描述语言进行编码，可以直接集成到用户的设计中。这个IP核经过精心优化，能够在FPGA的并行架构上高效运行，提供高性能和低延迟的FFT运算。在描述中提到的硬件测试代码，是验证Xilinx FFT IP核正确性的关键步骤。这通常包括设置输入数据，调用FFT核进行计算，并与软件模拟的计算结果进行比较。确保硬件实现与理论预期一致，这是硬件设计中不可或缺的质量保证环节。在FPGA实现FFT的过程中，我们需要注意以下几点： 1. **配置参数**：根据应用需求选择合适的FFT长度，如128、256、512等，同时考虑输入和输出的数据格式，如固定点或浮点。 2. **数据流优化**：为了最大化FPGA的并行性能，需要合理设计数据流路径，避免瓶颈和不必要的延迟。 3. **资源分配**：合理分配FPGA的逻辑资源，如查找表（LUTs）、触发器（FFs）和分布式RAM，以实现最佳的面积和速度权衡。 4. **时序分析**：通过综合和时序分析工具，确保设计满足时钟周期约束，以达到预期的工作频率。 5. **功耗管理**：考虑低功耗设计策略，如动态电压和频率调整（DVFS）以及在不活动时关闭部分逻辑。在压缩包中的"fft"文件很可能是包含测试代码、配置文件或生成的比特流文件。这些文件对于理解和复现测试过程至关重要，它们记录了具体的设计细节和实现方式。总结来说，Xilinx的FFT IP核提供了在FPGA上实现高效FFT运算的途径，结合硬件测试代码的验证，确保了设计的准确性和可靠性。通过深入理解FFT算法、优化FPGA设计以及充分利用Xilinx的IP核，我们可以构建出满足各种需求的高速、低延迟的信号处理系统。

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xilinx FFT （255个子文件）

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a_3622719947_2372691052.nt64.a 1.49MB

FFT.asy 2KB

a_2267914804_2098929354.c 2.06MB

a_2777468956_0319854582.c 1.49MB

p_1367372525.c 1.18MB

p_1242562249.c 788KB

p_3499444699.c 574KB

a_4291375251_3224734547.c 569KB

a_0472234004_3224734547.c 569KB

p_2717149903.c 284KB

p_2592010699.c 192KB

p_3222816464.c 183KB

p_3564397177.c 148KB

p_0774719531.c 64KB

p_3620187407.c 59KB

a_2335479032_3672491681.c 45KB

a_0415367962_0632001823.c 45KB

a_2548838872_3634591992.c 41KB

a_2580025623_3676810390.c 27KB

a_0490859109_3731405331.c 24KB

a_2840836969_0170033766.c 16KB

a_1446710196_3752513572.c 13KB

a_1916428545_3403034321.c 13KB

a_1398595224_1990404599.c 12KB

a_2680519808_1064626918.c 8KB

a_1566006631_3088576162.c 7KB

tb_TOP_isim_beh.exe_main.c 6KB

a_2261302797_3723259517.c 4KB

a_3622719947_2372691052.c 4KB

a_3586452736_3089378898.c 4KB

a_0868425105_1864968857.c 4KB

a_4186627520_3212880686.c 4KB

a_1941169844_3118875749.c 3KB

a_0350208134_1521797606.c 3KB

a_3519694068_2693788048.c 3KB

a_1646226234_1266530935.c 2KB

a_1306507066_0234597810.c 2KB

a_2456189163_0111667855.c 2KB

a_3055263662_1392679692.c 2KB

a_3484885994_2523279426.c 2KB

a_2562466605_1496654361.c 2KB

a_1717296735_4086321779.c 2KB

a_1335952598_3212880686.c 1KB

p_0947159679.c 1KB

textio.c 1KB

coregen.cgp 236B

fuseRelaunch.cmd 154B

isim.cmd 44B

data.cmd_log 157B

TOP.cmd_log 96B

netId.dat 196B

ISimEngine-DesignHierarchy.dbg 1.79MB

a_0415367962_0632001823.didat 1.83MB

a_2267914804_2098929354.didat 204KB

a_2777468956_0319854582.didat 195KB

a_0472234004_3224734547.didat 102KB

a_4291375251_3224734547.didat 102KB

a_2840836969_0170033766.didat 47KB

a_1566006631_3088576162.didat 41KB

p_3222816464.didat 15KB

p_1367372525.didat 15KB

p_2717149903.didat 12KB

p_1242562249.didat 8KB

a_1335952598_3212880686.didat 7KB

a_3622719947_2372691052.didat 6KB

p_2592010699.didat 6KB

p_0947159679.didat 5KB

a_1916428545_3403034321.didat 5KB

a_1398595224_1990404599.didat 5KB

p_3564397177.didat 5KB

p_3499444699.didat 4KB

a_2680519808_1064626918.didat 4KB

a_3888678762_3634591992.didat 4KB

a_2414330276_3634591992.didat 4KB

a_2829679298_3634591992.didat 4KB

a_1879069463_3634591992.didat 4KB

a_0029328054_3634591992.didat 4KB

a_2548838872_3634591992.didat 4KB

a_2870136864_3634591992.didat 4KB

a_4026352213_3634591992.didat 4KB

a_0128924074_3634591992.didat 4KB

a_3952243180_3634591992.didat 4KB

a_0773895216_3634591992.didat 4KB

a_1340215100_3634591992.didat 4KB

a_4141099143_3634591992.didat 4KB

a_2704037000_3634591992.didat 4KB

a_2061463046_3634591992.didat 4KB

a_0000397353_3634591992.didat 4KB

a_2335479032_3672491681.didat 4KB

a_4072589784_3672491681.didat 4KB

a_0382489742_3672491681.didat 4KB

a_0468036963_3672491681.didat 4KB

a_2093478228_3672491681.didat 4KB

a_2203725902_3676810390.didat 4KB

a_2363982922_3676810390.didat 4KB

a_3062228385_3676810390.didat 4KB

a_3912374477_3676810390.didat 4KB

a_4273656797_3676810390.didat 4KB

a_2503372758_3676810390.didat 4KB

共 255 条

DS260 March 1, 2011 www.xilinx.com 1

Product Specification

United States and other countries. MATLAB is a registered trademark of The MathWorks, Inc. All other trademarks are the property of their respective owners.

Introduction

The Xilinx LogiCORE™ IP Fast Fourier Transform

(FFT) implements the Cooley-Tukey FFT algorithm, a

computationally efficient method for calculating the

Discrete Fourier Transform (DFT).

Features

• Drop-in module for Kintex™-7, Virtex

-7, Virtex®-6,

Virtex-5, Virtex-4, Spartan®-6, Spartan-3/XA,

Spartan-3E/XA and Spartan-3A/XA/AN/3A DSP

FPGAs

• Forward and inverse complex FFT, run-time

configurable

• Transform sizes N = 2

, m = 3 – 16

• Data sample precision b

= 8 – 34

• Phase factor precision b

= 8 – 34

• Arithmetic types:

¨ Unscaled (full-precision) fixed-point

¨ Scaled fixed-point

¨ Block floating-point

• Fixed-point or floating-point interface

• Rounding or truncation after the butterfly

• Block RAM or Distributed RAM for data and

phase-factor storage

• Optional run-time configurable transform point

size

• Run-time configurable scaling schedule for scaled

fixed-point cores

• Bit/digit reversed or natural output order

• Optional cyclic prefix insertion for digital

communications systems

• Four architectures offer a trade-off between core

size and transform time

• Bit-accurate C model and MEX function for system

modeling available for download

• For use with Xilinx CORE Generator™ software

and Xilinx System Generator for DSP v13.1

LogiCORE IP

Fast Fourier Transform v7.1

DS260 March 1, 2011 Product Specification

LogiCORE IP Facts Table

Core Specifics

Supported

Device Family

(1)

1. For a complete listing of supported devices, see the release notes

for this core.

Virtex-7 and Kintex-7,

Virtex-6, Virtex-5, Virtex-4,

Spartan-6, Spartan-3/XA, Spartan-3E/XA,

Spartan-3A/3AN/3A DSP/XA

Supported User

Interfaces

Not Applicable

Resources

(2)

2. Resources listed here are for Virtex-6 devices. For more complete

device performance numbers, see Table 7-Table 10.

Frequency

Configuration LUTs FFs

DSP

Slices

Block

RAMs

(3)

3. Based on 18K block RAMs.

Max.

Freq.

(4)

4. Performance numbers listed are for Virtex-6 FPGAs. For more

complete performance data, see Table 7-Table 10.

Radix-2,

2048-point,

variable point

size, 16-bit

data/phase

factors, scaled,

convergent

rounding

1092 1265 3 5 395 MHz

Provided with Core

Documentation

Product Specification

Design Files Netlist

Example Design Not Provided

Test Bench Not Provided

Constraints File Not Applicable

Simulation

Model

VHDL behavioral model in the xilinxcorelib library

VHDL UniSim structural model

Verilog UniSim structural model

Tested Design Tools

Design Entry

Tools

CORE Generator tool 13.1

System Generator for DSP 13.1

Simulation

Mentor Graphics ModelSim 6.6d

Cadence Incisive Enterprise Simulator (IES) 10.2

Synopsys VCS and VCS MX 2010.06

ISIM 13.1

Synthesis Tools N/A

Support

Provided by Xilinx, Inc.

DS260 March 1, 2011 www.xilinx.com 2

Product Specification

LogiCORE IP Fast Fourier Transform v7.1

Overview

The FFT core computes an N-point forward DFT or inverse DFT (IDFT) where N can be 2

, m = 3–16.

For fixed-point inputs, the input data is a vector of N complex values represented as dual b

-bit two’s-complement

numbers, that is, b

bits for each of the real and imaginary components of the data sample, where b

is in the range

8 to 34 bits inclusive. Similarly, the phase factors b

can be 8 to 34 bits wide.

For single-precision floating-point inputs, the input data is a vector of N complex values represented as dual 32-bit

floating-point numbers with the phase factors represented as 24- or 25-bit fixed-point numbers.

All memory is on-chip using either block RAM or distributed RAM. The N element output vector is represented

using b

bits for each of the real and imaginary components of the output data. Input data is presented in natural

order and the output data can be in either natural or bit/digit reversed order. The complex nature of data input and

output is intrinsic to the FFT algorithm, not the implementation.

Three arithmetic options are available for computing the FFT:

• Full-precision unscaled arithmetic

• Scaled fixed-point, where the user provides the scaling schedule

• Block floating-point (run-time adjusted scaling)

The point size N, the choice of forward or inverse transform, the scaling schedule and the cyclic prefix length are

run-time configurable. Transform type (forward or inverse), scaling schedule and cyclic prefix length can be

changed on a frame-by-frame basis. Changing the point size resets the core.

Four architecture options are available: Pipelined, Streaming I/O, Radix-4, Burst I/O, Radix-2, Burst I/O, and

Radix-2 Lite, Burst I/O. For detailed information about each architecture, see "Architecture Options."

Theory of Operation

The FFT is a computationally efficient algorithm for computing a Discrete Fourier Transform (DFT) of sample sizes

that are a positive integer power of 2. The DFT of a sequence is defined as

Equation 1

where N is the transform size and . The inverse DFT (IDFT) is given by

Equation 2

Algorithm

The FFT core uses the Radix-4 and Radix-2 decompositions for computing the DFT. For Burst I/O architectures, the

decimation-in-time (DIT) method is used, while the decimation-in-frequency (DIF) method is used for the Pipe-

lined, Streaming I/O architecture. When using Radix-4 decomposition, the N-point FFT consists of log

(N) stages,

with each stage containing N/4 Radix-4 butterflies. Point sizes that are not a power of 4 need an extra Radix-2 stage

for combining data. An N-point FFT using Radix-2 decomposition has log

(N) stages, with each stage containing

N/2 Radix-2 butterflies.

The inverse FFT (IFFT) is computed by conjugating the phase factors of the corresponding forward FFT.

(), 0, , 1Xk k N=−K

(), 0, , 1xn n N=−K

() () 0, , 1

jnk N

Xk xne k N

−

==−

∑

1j =−

() () 0, , 1

jnk N

xn Xke n N

−

==−

∑

DS260 March 1, 2011 www.xilinx.com 3

Product Specification

LogiCORE IP Fast Fourier Transform v7.1

Finite Word Length Considerations

The Burst I/O architectures process an array of data by successive passes over the input data array. On each pass,

the algorithm performs Radix-4 or Radix-2 butterflies, where each butterfly picks up four or two complex numbers,

respectively, and returns four or two complex numbers to the same memory. The numbers returned to memory by

the core are potentially larger than the numbers picked up from memory. A strategy must be employed to accom-

modate this dynamic range expansion. A full explanation of scaling strategies and their implications is beyond the

scope of this document; for more information about this topic; see [Ref 1] and [Ref 2].

For a Radix-4 DIT FFT, the values computed in a butterfly stage can experience growth by a factor of up to

. This implies a bit growth of up to 3 bits.

For Radix-2, the growth is by a factor of up to . This implies a bit growth of up to 2 bits. This bit

growth can be handled in three ways:

• Performing the calculations with no scaling and carrying all significant integer bits to the end of the

computation

• Scaling at each stage using a fixed-scaling schedule

• Scaling automatically using block floating-point

All significant integer bits are retained when using full-precision unscaled arithmetic. The width of the data path

increases to accommodate the bit growth through the butterfly. The growth of the fractional bits created from the

multiplication are truncated (or rounded) after the multiplication. The width of the output is (input width +

log

(transform length) + 1). This accommodates the worst case scenario for bit growth.

Consider an unscaled Radix-2 DIT FFT: the data path in each stage must grow by 1 bit as the adder and subtractor

in the butterfly may add/subtract two full-scale values and produce a sample which has grown in width by 1 bit.

This yields the log

(transform length) part of the increase in the output width relative to the input width. The com-

plex multiplier preserves the magnitude of an input (as it applies a rotation on the complex plane), but can theoret-

ically produce bit-growth when the magnitude of the input is greater than 1 (for example, 1+j has a magnitude of

1.414). This means that the complex multiplier bit growth must only be considered once in the entire FFT process,

yielding the additional +1 increase in the output width relative to the input width. For example, a 1024-point trans-

form with an input of 16 bits consisting of 1 integer bit and 15 fractional bits has an output of 27 bits with 12 integer

bits and 15 fractional bits. Note that the core does not have a specific location for the binary point. The output sim-

ply maintains the same binary point location as the input. For the preceding example, a 16 bit input with 3 integer

bits and 13 fractional bits would have an unscaled output of 27 bits with 14 integer bits and 13 fractional bits.

When using scaling, a scaling schedule is used to divide by a factor of 1, 2, 4, or 8 in each stage. If scaling is insuffi-

cient, a butterfly output may grow beyond the dynamic range and cause an overflow. As a result of the scaling

applied in the FFT implementation, the transform computed is a scaled transform. The scale factor s is defined as

Equation 3

where b

is the scaling (specified in bits) applied in stage i.

The scaling results in the final output sequence being modified by the factor 1/s. For the forward FFT, the output

sequence X’ (k), k = 0,...,N - 1 computed by the core is defined as

Equation 4

132 5.242≈+

122.414+≈

log 1

−

∑

'2/

() () () 0, , 1

jnk N

Xk Xk xne k N

−

== =−

∑

DS260 March 1, 2011 www.xilinx.com 4

Product Specification

LogiCORE IP Fast Fourier Transform v7.1

For the inverse FFT, the output sequence is

Equation 5

If a Radix-4 algorithm scales by a factor of 4 in each stage, the factor of 1/s is equal to the factor of 1/N in the inverse

FFT equation (Equation 2). For Radix-2, scaling by a factor of 2 in each stage provides the factor of 1/N.

With block floating-point, each stage applies sufficient scaling to keep numbers in range, and the scaling is tracked

by a block exponent.

As with unscaled arithmetic, for scaled and block floating-point arithmetic, the core does not have a specific loca-

tion for the binary point. The location of the binary point in the output data is inherited from the input data and

then shifted by the scaling applied.

Floating-Point Considerations

The FFT core optionally accepts data in IEEE-754 single-precision format with 32-bit words consisting of a 1-bit

sign, 8-bit exponent, and 23-bit fraction. The construction of the word matches that of the

Xilinx Floating-Point Operator core.

Implementing full floating-point on an FPGA can be expensive in terms of the resources required. The float-

ing-point option in the Xilinx FFT core utilizes a higher precision fixed-point FFT internally to achieve similar noise

performance to a full floating-point FFT, with significantly fewer resources. Figure 1 illustrates the two levels of

noise performance possible by selecting either 24 bits or 25 bits for the phase factor width. By increasing the phase

factor width to 25 bits, more resources may be required, depending on the target FPGA device.

X-Ref Target - Figure 1

Figure 1: Comparison of Two Levels of Noise Performance

() () 0, , 1

jnk N

xn X ke n N

−

==−

∑

DS260 March 1, 2011 www.xilinx.com 5

Product Specification

LogiCORE IP Fast Fourier Transform v7.1

Figure 1 shows the ratio of the RMS difference between various models and the double-precision MATLAB® FFT to

the data set peak amplitude. The models shown are the single-precision MATLAB FFT function (calculated by

casting the input data to single-precision floating-point type), the Xilinx FFT core using a 24-bit phase factor width,

and the Xilinx FFT core using a 25-bit phase factor width. To calculate the error signal, a randomized impulse (in

magnitude and time) was used as the input signal, with the RMS error averaged over five simulation runs.

All optimization options (memory types and XtremeDSP™ slice optimization) remain available when float-

ing-point input data is selected, allowing the user to trade off resources with transform time.

Transform time for Burst I/O architectures is increased by approximately N, the number of points in the transform,

due to the input normalization requirements. For the Pipelined, Streaming I/O architecture, the initial latency to fill

the pipeline is increased, but data still streams through the core with no gaps.

Denormalized Numbers

The floating-point interface to the FFT core does not support denormalized numbers. To match the behavior of the

Xilinx Floating-Point Operator core, the core treats denormalized operands as zero, with a sign taken from the

denormalized number.

NaNs and ± Infinity

If the core detects a NaN or ± Infinity value on the input, all output samples associated with the current input frame

are set to NaN. The sign bit is set to zero and all exponent and fraction bits are set to 1.

Real-Valued Input Data

The FFT core accepts complex data samples, but can perform a transform on real-valued data by setting all imagi-

nary input samples to zero.

Due to the finite wordlength effects described previously, noise is introduced during the transform, resulting in the

output data not being perfectly symmetric. The DIT and DIF FFT algorithms have different noise effects due to the

different calculation order.

For a thorough treatment of this topic, see [Ref 3] and [Ref 4].

The asymmetry between the two halves of the result is more noticeable at larger point sizes. In addition, the noise

is more prominent in the lower frequency bins. Therefore, Xilinx recommends that the upper half (N/2+1 to N

points) of the output data is used when performing a real-valued FFT.

Rounding Implementation

An option is available, in all architectures, to apply convergent rounding to the data after the butterfly stage. How-

ever, selecting this option does not apply convergent rounding to all points in the data path where wordlength

reduction occurs.

In particular, the outputs of all complex multipliers in the FFT data path are truncated to reduce data path width

(while still maintaining adequate precision) and a simple rounding constant added to the fractional bits. This con-

stant implements non-symmetric, round-towards-minus-infinity rounding, and can introduce a small bias to the

FFT results over a large number of samples.

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