FFT IP Core: User Guide

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ID 683374
Date 11/06/2017
Public
Document Table of Contents
Document Table of Contents x
1. About This IP Core 2. FFT IP Core Getting Started 3. FFT IP Core Functional Description 4. Block Floating Point Scaling 5. Document Revision History A. FFT IP Core User Guide Document Archive
1. About This IP Core x
1.1. Intel® DSP IP Core Features 1.2. FFT IP Core Features 1.3. General Description 1.4. DSP IP Core Device Family Support 1.5. DSP IP Core Verification 1.6. FFT IP Core Release Information 1.7. Performance and Resource Utilization
1.3. General Description x
1.3.1. Fixed Transform Size FFT 1.3.2. Variable Streaming FFT
2. FFT IP Core Getting Started x
2.1. Installing and Licensing Intel® FPGA IP Cores 2.2. IP Catalog and Parameter Editor 2.3. Generating IP Cores ( Intel® Quartus® Prime Pro Edition) 2.4. Generating IP Cores ( Intel® Quartus® Prime Standard Edition) 2.5. Simulating Intel® FPGA IP Cores 2.6. DSP Builder for Intel® FPGAs Design Flow
2.1. Installing and Licensing Intel® FPGA IP Cores x
2.1.1. Intel® FPGA IP Evaluation Mode 2.1.2. FFT IP Core Intel® FPGA IP Evaluation Mode Timeout Behavior
2.3. Generating IP Cores ( Intel® Quartus® Prime Pro Edition) x
2.3.1. IP Core Generation Output ( Intel® Quartus® Prime Pro Edition)
2.4. Generating IP Cores ( Intel® Quartus® Prime Standard Edition) x
2.4.1. IP Core Generation Output ( Intel® Quartus® Prime Standard Edition)
2.5. Simulating Intel® FPGA IP Cores x
2.5.1. Simulating the Fixed-Transform FFT IP Core in the MATLAB Software 2.5.2. Simulating the Variable Streaming FFT IP Core in the MATLAB Software
3. FFT IP Core Functional Description x
3.1. Fixed Transform FFTs 3.2. Variable Streaming FFTs 3.3. FFT Processor Engines 3.4. I/O Data Flow 3.5. FFT IP Core Parameters 3.6. FFT IP Core Interfaces and Signals
3.2. Variable Streaming FFTs x
3.2.1. Fixed-Point Variable Streaming FFTs 3.2.2. Floating-Point Variable Streaming FFTs
3.3. FFT Processor Engines x
3.3.1. Quad-Output FFT Engine 3.3.2. Single-Output FFT Engine
3.4. I/O Data Flow x
3.4.1. Streaming FFT 3.4.2. Variable Streaming 3.4.3. Buffered Burst 3.4.4. Burst
3.4.1. Streaming FFT x
3.4.1.1. Using the Streaming FFT 3.4.1.2. Changing the Direction on a Block-by-Block Basis 3.4.1.3. Enabling the Streaming FFT
3.4.2. Variable Streaming x
3.4.2.1. Changing Block Size 3.4.2.2. Changing Direction 3.4.2.3. I/O Order 3.4.2.4. Enabling the Variable Streaming FFT 3.4.2.5. Dynamically Changing the FFT Size 3.4.2.6. I/O Order
3.4.3. Buffered Burst x
3.4.3.1. Enabling the Buffered Burst FFT
3.6. FFT IP Core Interfaces and Signals x
3.6.1. Avalon-ST Interfaces in DSP IP Cores 3.6.2. FFT IP Core Avalon-ST Signals
3.6.2. FFT IP Core Avalon-ST Signals x
3.6.2.1. Component Specific Signals
4. Block Floating Point Scaling x
4.1. Possible Exponent Values 4.2. Implementing Scaling 4.3. Unity Gain in an IFFT+FFT Pair
4.2. Implementing Scaling x
4.2.1. Example of Scaling
1. About This IP Core
2. FFT IP Core Getting Started
3. FFT IP Core Functional Description
4. Block Floating Point Scaling
5. Document Revision History
A. FFT IP Core User Guide Document Archive

3.2. Variable Streaming FFTs

The variable streaming FFTs use fixed-point data representation or the floating point representation.

If you select the fixed-point data representation, the FFT variation uses a radix 22 single delay feedback, which is fully pipelined. If you select the floating point representation, the FFT variation uses a mixed radix-4/2. For a length N transform, log4(N) stages are concatenated together. The radix 22 algorithm has the same multiplicative complexity of a fully pipelined radix-4 FFT, but the butterfly unit retains a radix-2 FFT. The radix-4/2 algorithm combines radix-4 and radix-2 FFTs to achieve the computational advantage of the radix-4 algorithm while supporting FFT computation with a wider range of transform lengths. The butterfly units use the DIF decomposition.

Fixed point representation allows for natural word growth through the pipeline. The maximum growth of each stage is 2 bits. After the complex multiplication the data is rounded down to the expanded data size using convergent rounding. The overall bit growth is less than or equal to log2(N)+1.

The floating point internal data representation is single-precision floating-point (32-bit, IEEE 754 representation). Floating-point operations provide more precise computation results but are costly in hardware resources. To reduce the amount of logic required for floating point operations, the variable streaming FFT uses fused floating point kernels. The reduction in logic occurs by fusing together several floating point operations and reducing the number of normalizations that need to occur.


Section Content
Fixed-Point Variable Streaming FFTs
Floating-Point Variable Streaming FFTs

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