Fixed-Point Made Easy for FPGA Programming

版本 2.0.0.0 (3.1 MB) 作者: MathWorks Fixed Point Team
Material used in the "Fixed-Point Made Easy for FPGA Programming" webinar.

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更新时间 2020/10/21

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One of the biggest challenges in FPGA programming is the process of quantizing mathematical operations to fixed-point for more efficient implementation.

This session teaches the fundamentals of the fixed-point number system and fixed-point arithmetic, along with considerations for targeting popular FPGA devices. These concepts are then reinforced through practical demonstrations, capped by walking through the process of quantizing a signal processing design.

Topics include:

Fixed-point theory
Fixed-point number system
Mathematical range
Quantization error in the time and frequency domains
Common functions
Arithmetic: square root, reciprocal, log2
Trigonometry: cosine, sine, atan2
Signal processing: FIR, FFT
FPGA considerations
Targeting Xilinx and Intel devices
Maintaining precision
Using native floating point for full-precision calculations
Example: communications packet detection
Matched filter
Peak detection
FPGA optimizations

引用格式

MathWorks Fixed Point Team (2022). Fixed-Point Made Easy for FPGA Programming (https://www.mathworks.com/matlabcentral/fileexchange/64495-fixed-point-made-easy-for-fpga-programming), MATLAB Central File Exchange. 检索来源 .

MATLAB 版本兼容性
创建方式 R2017b
兼容 R2017b 及更高版本
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