Fixed-Point Made Easy for FPGA Programming

Material used in the "Fixed-Point Made Easy for FPGA Programming" webinar.
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Aktualisiert 21. Okt 2020

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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

Zitieren als

MathWorks Fixed Point Team (2024). 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. Abgerufen .

Kompatibilität der MATLAB-Version
Erstellt mit R2017b
Kompatibel mit R2017b und späteren Versionen
Plattform-Kompatibilität
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Version Veröffentlicht Versionshinweise
2.0.0.0

Updated the material used in the webinar.

1.0.0.0

Added copyright notices.