CSE 237C - Validation&Testing/EmbeddedSys - Kastner [FA24]

Class Description: This class focuses on creating embedded system prototypes using a programmable system-on-chip (SoC). The class is graded primarily based on the performance in projects that are spread across the class. The projects require the student to implement a hardware-accelerated core and integrate it into a prototype SoC. Students understand parallel programming concepts, learn how to use modern high-level synthesis tools, and develop hardware-accelerated compute systems.

Class Materials:

• Book: Parallel Programming for FPGAs
• Projects and Labs
• Board: Pynq Z2
• Lectures
• Xilinx Software Installation

Late Submission Policy:

I will give everyone in this class two late days to use throughout the quarter for their projects. A late day must be used in increments of one day. One can use both late days on a single assignment.

To make this clear through examples:

• You can turn in one project one day late (using one late day) and use the other late day on a future project
• You can turn in one project two days late and must turn in all future projects on time.

You do not need to tell us if you use a late day.

If you have used up your late days and turned in a project late, you will get partial credit on that assignment. The penalty is 20% per day, i.e., turn it in less than one day late, and the best you can get is 80% on the assignment. 

We will automatically deduct your late days first before going into partial credit. 

Generative AI Policy:

Generative artificial intelligence tools—software that creates new text, images, computer code, audio, video, and other content—have become widely available. Well-known examples include ChatGPT for text and DALL•E for images. This policy governs all such tools, including those released during our quarter together.

You may use generative AI tools on assignments in this course within the following limitations. If you use generative AI tools on assignments in this class, you must properly document and credit the tools themselves. You must cite the tool you used. Additionally, please include a brief description of how you used the tool. If you choose to use generative AI tools, please remember that they are typically trained on limited datasets that may be outdated.

Generative AI datasets are trained on pre-existing material, including copyrighted material; therefore, relying on a generative AI tool may result in plagiarism or copyright violations.

Finally, remember that generative AI tools aim to produce content that seems to have been produced by a human, not to produce accurate or reliable content; therefore, relying on a generative AI tool may result in your submission of inaccurate content. It is your responsibility—not the tool’s—to assure the quality, integrity, and accuracy of work you submit in any college course.

We reserve the right to use software to detect AI-generated text and code. If you use generative AI tools to complete assignments in this course in ways that are not explicitly authorized, you will be subject UCSD Academic Integrity Policy as appropriate to your specific case. In addition, you must be wary of unintentional plagiarism or fabrication of data. Please act with integrity, for the sake of both your personal character and your academic record.