Method
Live-Online
Term
FALL
Units
3.0 QUARTER UNITS
Estimated Cost
$990

Skills you will gain

  • Autonomous Driving Fundamentals: Learn core concepts in machine learning, computer vision, and AI for self-driving systems.
  • FPGA-Based AI Implementation: Develop and implement AI-driven image processing algorithms on FPGA hardware.
  • Object & Lane Detection: Design and optimize vision-based algorithms for detecting vehicles, lanes, and traffic signs.
  • RTL & Verilog for FPGA: Build real-time digital logic designs using Verilog for AD system applications.
  • System Simulation & Verification: Test, verify, and synthesize FPGA-based autonomous driving solutions for real-world deployment.

Course Description

AI is driving the shift to autonomous vehicles, and this course puts you at the center of it. Learn object detection, lane detection, and machine learning fundamentals, then implement them on an FPGA using Verilog, gaining hands-on hardware design experience.

Prerequisites / Skills Needed

Prerequisites:

  • VLSI.X404: Digital Logic Design Using Verilog
Syllabus Library
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This course applies to these programs:

Demo