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Fixed-Point Made Easy for FPGA Programming

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

Related Products

  • Fixed-Point Designer
  • HDL Coder

Learn More

Best practices for Converting MATLAB Code to Fixed Point using Fixed-Point Designer
What Is Quantization?
Fixed-Point Made Easy for FPGA Programming

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Best practices for Converting MATLAB Code to Fixed Point...

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