Design AI-powered autonomous driving systems with hands-on FPGA and machine learning skills.
Units
3.0 QUARTER UNITS
Estimated Cost
$980
Course Description
The Silicon Valley-led shift from conventional, fully human-driven vehicles to autonomous driving (AD) systems empowered by artificial intelligence has created a huge demand for engineers and researchers who know these emerging technologies.
In this course, which is open to students with a basic knowledge of programming and digital logic, we will explore the fundamentals of AD systems—machine learning, computer vision, and hardware implementation on a field programmable gate array (FPGA). We will cover critical concepts such as object, vehicle, and lane detection, as well as traffic sign classification, AI, and deep learning algorithms. You will study practical systematic design of typical FPGA applications in AD systems using the hardware description language, Verilog before moving to testbench development, simulation for bit-true design verification, and complete system design synthesis.
The course is designed to strengthen theoretical understanding and provide hands-on experience with hardware. By the end of the course, you will have hands-on experience with FPGA design and be able to design, test, and implement a complete digital system on an FPGA device including interfacing to external devices.
Prerequisites / Skills Needed
Some programming knowledge (Python, MATLAB, VHDL, Verilog, SystemVerilog) and digital system design experience is preferred, but not required.
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