ABSTRACT
The immediate and new challenges of the current Covid-19 pandemic have made it hard for all of us;in this work in progress as an innovative practice, we look to both leverage the challenges and share our work so that others might see some benefit to these times and improve their courses. In particular, our focus is on creating automated tools to physically create exams and sample code for Digital Systems and Computer Architecture courses. Additionally, we focus on shifting, traditional in-person labs to online, personalized formats for Digital Systems and Embedded Systems so that both educator and learner can still provide/experience virtual computer engineering education. We focus on three courses (Digital System Design, Computer Architecture/Organization, and Embedded System Design) as they are fundamentally driven by the implementation and execution of "algorithms". From this starting point, we have created tools to generate sample code and exams, and have found means to virtualize labs and hands-on activities. In particular, we have created Python tools that allow educators to personalize code and problems, create these codes/problems (as text files or incorporated in word documents), and email these documents to students. This provides the means to create problems and code examples that are different from their peers and can be assessed on a per individual basis to alleviate some of the challenges with live and proctored exams. Additionally, we have found tools and methods for students to virtually perform the hands-on portion of these three subjects without the need for traditional lab equipment. This requires students to spend less than 100 USD worth of equipment and software. Our goal is to share these resources and our methodologies to help in this time of crisis. Additionally, these tools and methods have forced us to innovate our teaching, and we will, likely, use these tools and methods in the future. We share these tools in hope that the computer engineering education community will join this process to help us all improve our student's education.
ABSTRACT
Google Colab is a cloud Jupyter notebook widespread used to teach machine learning by writing text explanations and Python codes through the browser. This work introduces new Colab extensions to teach logic circuit design, Verilog language, processor, and GPU architectures. Colab allows us to share reproducible experiments on the Web. The students become motivated to do laboratory assignments without download/configure software packages and dependencies on their computers. Furthermore, almost all universities had to shut down due to the COVID-19 pandemic, forcing us to adapt to virtual learning scenarios. Colab provides portability and accessibility since it can even run on smartphones. The lab assignments include intermediate guided exercises, text explanations, figures, online quizzes, problem sets, and basic hands-on tasks. We develop a simple setup for Icarus Verilog, PyEDA, CUDA, Valgrind, and Gem5 frameworks. This work presents Verilog teaching and computer architecture simulation insights by using Valgrind and Gem5, and GPU computer architecture profiling at the thread and instruction assembly level.