Essential DSP Implementation Techniques for Xilinx FPGAs

BLT offers a newer course for this material: Essential DSP Design Techniques Using System Generator.

This course provides a foundation for Digital Signal Processing (DSP) implementation techniques for Xilinx® FPGAs. The course begins with a refresher of basic binary number theory, mathematics, and the essential features within the FPGA that are important to signal processing. The body of the course explores a variety of filter techniques with emphasis on optimal implementation in Xilinx devices and continues with an examination of FFTs, video, and image processing. Throughout the course, Xilinx cores and IP relevant to signal processing are introduced. The course is complemented by hands-on exercises to reinforce the concepts learned.

Learn more about Xilinx DSP implementation.

See Course Outline

1-Day Instructor-led CoursePrice USDTraining Credits
Hosted Online - $299/day$2993
In-Person Registration - $399/day$3994
Printed Course Book (mailed to you)$1001
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Scheduled Classes

No Scheduled Sessions - Contact Us to ask about setting one up!

Training Duration:

1 Day

We update our schedule regularly. Stay informed.

Who should attend:

Engineers and designers who have an interest in developing products that use digital signal processing.

Skills Gained

After completing this comprehensive training, you will know how to:

  • Describe the advantages of using FPGAs over traditional processors for DSP designs
  • Utilize fixed point binary arithmetic and identify how to use this knowledge to create efficient designs in FPGAs
  • Recognize how both the CLB slices in FPGAs and the more advanced DSP48s are used to implement DSP algorithms
  • Explain the dataflow through the device and how to use distributed memory, block RAM, registers, and SRLs to properly implement these designs
  • Construct different FIR filter and FFT implementations and how to optimize these implementations in the FPGA
  • Explain the algorithms for video and imaging systems and their implementations in FPGAs

Course Outline

Day 1
  • Back to Basics
  • Architecture
  • FPGA Math
  • LAB: Signed Number Conversion, Quantization and Rounding, Adders, Subtractors, and Accumulation
    Learn how to estimate device resource utilization for basic math functions. Compare different methodologies for implementing functions.
  • Shift Registers, RAM, and Applications
  • LAB: SRL32E and RAM Estimation and Concatenation
    Learn how to optimize memory and storage in Xilinx FPGAs.
  • FIR Filter
  • LAB: Filter Implementation, Resource and Performance Estimation
    Learn how and when to use various implementation strategies for optimal filter implementation.
  • Advanced Filter Techniques
  • LAB: Filter Implementations, Resource and Performance Estimation
    Advanced filter topologies are studied. Architect multichannel and multirate filters using various methods. Implementation strategies will be discussed and optimal methods used.
  • Fast Fourier Transform
  • LAB: FFT Implementation, Resource and Performance Estimation
    Select correct parameters for FFT implementations to meet design targets. Resource estimation will be studied and trade-offs with performance examined through implementation examples.
  • Video and Imaging
  • Where Do We Go From Here?
  • Demonstration: System Generator and the CORE Generator™ Tool with a DSP-Targeted Reference Design
    Targeted Reference Design Introduces DSP-targeted hardware boards and software tools. Witness the power, ease of use, and design efficiency of Xilinx DSP tools and IP. Reinforce the concepts studied in the course material and exercises.
  • Where Can I Learn More?

Please note: The instructor may change the content order to provide a better learning experience.


  • A fundamental understanding of digital signal processing theory, including an understanding of the following principles
  • Sample rates
  • Finite Impulse Response (FIR) and Infinite Impulse Response (IIR) filters
  • Oscillators and mixers
  • Fast Fourier Transform (FFT) algorithm


Updated 4-10-22
©2022 Advanced Micro Devices, Inc. Xilinx, Inc. is now part of AMD. Xilinx, the Xilinx logo, AMD, the AMD Arrow logo, Alveo, Artix, Kintex, Kria, Spartan, Versal, Vitis, Virtex, Vivado, Zynq, and other designated brands included herein are trademarks of Advanced Micro Devices, Inc.