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Digital Signal Processing (DSP) Techniques with VHDL

A 2-day introductory DSP with VHDL course for engineers who want to understand how digital signal processing algorithms move from modelling to FPGA implementation. Includes a live digital FIR filter design demo, plus the core maths behind FFT, convolution and the z-transform.

Synopsis

Digital signal processing (DSP) is the backbone of many technological fields, such as wired and wireless communication, military and intelligence gathering, industrial process control, medical science, cryptography and transportation. DSP algorithms such as spectral analysis and Kalman filtering, traditionally executed on specialised digital signal processors, are now increasingly carried out on FPGAs for their greater processing capability, increased chip-level integration and lower power consumption. The move towards FPGAs also means DSP algorithms are now "coded" in hardware description languages such as VHDL rather than assembly, C or C++.

This course gives an introduction to DSP and how its applications are implemented using hardware-based field-programmable gate arrays (FPGAs).

Course Highlights

Participants gain introductory knowledge of the tools and processes used to model and build practical DSP applications on FPGAs, including a live digital FIR filter hardware design demo.

What You Will Learn in This DSP with VHDL Course

This short course covers both theory and practice. Upon completing the course, participants will be able to:
  • Understand how DSP systems are modelled, and which tools are used for modelling.
  • Understand how DSP systems are implemented in real-life applications, which software tools are used for such implementations, and which hardware platforms are suitable for implementing DSP systems.

Who Should Attend

This course is particularly suitable as an introductory-level course in digital signal processing (DSP) for engineers and scientists. It is also suitable for advanced DSP engineers and scientists who want to find out what other industrial methods are used to implement DSP solutions.

Prerequisites

Participants should have a diploma/degree in electronics (or related) engineering with an understanding of digital signal processing. Working knowledge of digital systems is encouraged, but not required.

Course Methodology

This course is presented in a workshop style with example-led lectures interlaced with demonstrations for maximum understanding.

Course Duration

Two (2) days, 0900 to 1700.

Course Structure

  1. Introduction
    • Overview of digital signal processing.
    • Discrete-time vs. continuous-time: Analogies and similarities.
  2. Applications of DSP
    • Digital filtering and equalisation.
    • Source coding: data compression, encryption, scrambling.
    • Image/video processing, audio/speech processing.
    • Digital communications systems.
    • Digital control systems.
  3. Development phases in DSP designs
    • The modelling phase.
    • The implementation phase.
  4. The modelling phase of DSP development
    • Software modelling tools.
    • An example with Sage Math.
  5. The implementation phase of DSP development
    • Implementation options: Hardware or Software?
    • Advantages and disadvantages of hardware vs. software development of DSP systems.
    • An example hardware DSP design using FPGAs: a digital FIR filter.
    • Demo: Digital FIR filter — a DSP FPGA hardware design example.
    • The FPGA / ASIC DSP design and implementation flow.
  6. An overview of some DSP concepts
    • Discrete-time (DT) vs. continuous-time (CT) systems and notations.
    • Difference equations.
    • Convolution sum: A widely-used concept for DSP designs.
      • The unit impulse excitation and response for determining system characteristics.
      • Concept of convolution.
      • Similarities between the DT convolution sum and the CT convolution integral.
      • Responses of systems to other standard impulse excitations.
    • The Fourier series.
    • Time-to-frequency transformations: the Laplace, Fourier, and z transforms.
    • The Laplace transform.
    • The Fourier transform.
      • The discrete Fourier transform (DFT).
      • The fast Fourier transform (FFT): a popular DFT algorithm.
    • The z-transform.
    • Similarities between the DT z-transform, and the CT transforms such as Laplace (s-domain) and Fourier transforms.

Related VHDL Courses

Need to verify your DSP designs? Continue with VHDL Simulations and Testbenches, or browse all courses on our Education page.

Frequently Asked Questions

Is this DSP course suitable for beginners?
Yes. It is an introductory-level DSP course for engineers and scientists. Advanced DSP engineers also benefit from seeing how DSP solutions are implemented on FPGAs in industry.
Why implement DSP on an FPGA instead of a DSP processor?
FPGAs offer greater processing capability, higher chip-level integration and lower power consumption, so DSP algorithms such as spectral analysis and Kalman filtering are increasingly implemented in hardware using VHDL.
How long is the DSP with VHDL course?
The course runs for two days, from 0900 to 1700 each day, and includes a live digital FIR filter FPGA design demo.
What are the prerequisites?
Participants should have a diploma/degree in electronics or related engineering with an understanding of digital signal processing. Working knowledge of digital systems is encouraged but not required.