EEL 5769 Computer Architecture

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Florida Gulf Coast University
U.A. Whitaker College of Engineering
Department of Computing & Software Engineering

Course Catalog Description:

This course focuses on advanced topics, including memory hierarchy such as caching and virtual memory, pipelining, multiprocessor systems, instruction, data, and thread level parallelism and other contemporary issues in computer architecture.

Prerequisites:

Required Material

Textbook:

    (Recommended)
  • Andrew S. Tanenbaum, Structured Computer Organization, 6th Edition, Pearson, ISBN 978-0-13-291652-3.
  • John L. Hennessy and David A. Patterson, Computer Architecture: A Quantitative Approach, 5th Edition, Morgan Kaufmann, ISBN 978-0-12-383872-8

Learning Outcomes:

  1. Calculate and interpret different performance and cost metrics of computer systems.
  2. Analyze multiprocessors and clusters, shared memory address and network architectures.
  3. Analyze and compare different cache architectures, virtual memory, and/or identify the most suitable cache design for a given need.
  4. Analyze the control and data flow within a single-cycle cpu, multicycle implementation, and pipelined systems.
  5. Analyze the instructional level parallelism in the design of advanced pipelines.
  6. Analyze the data level parallelism in the design of advanced pipelines.
  7. Analyze the thread-level parallelism in the design of advanced pipelines.
  8. Explain or analyze contemporary issues in computer architecture.

Instructional Methods:

This is a face-to-face course with lectures, instructor led examples, and in-class labs.

Attendance:

Attendance is mandatory for all classes. Attendance will be taken and will count towards participation credit. Students should notify me at least 48 hours in advance of intent to miss a class to arrange alternate schedules for in-class assignments.

Communication:

All course related communication should be via Canvas message. Each student is responsible for checking his/her FGCU email at least once a day. The instructor will respond to your emails within 24 hours except weekends and holidays.

Topics / Tentative Schedule:

  • Module 1 — Foundations & Digital Systems (Weeks 1–2)
  • Module 2 — ISA & Microarchitecture (Weeks 3–5)
  • Module 3 — Memory Hierarchy (Weeks 6–8)
  • Module 4 — Instruction‑Level Parallelism (Weeks 9–10)
  • Module 5 — Data‑ & Thread‑Level Parallelism (Weeks 10–13)
  • Module 6 — Capstone & Current Directions (Week 16)

Assessment:

There is no grace period for late submission of assignments. All assignments with due dates and times will be posted on Canvas. Alternate schedules can be arranged ahead of time with instructor approval.

Working Independently:

Students are to work independently on all non-group assignments. It is cheating to see other classmate's work or let other classmates see yours. This does not mean you cannot assist or discuss assignments with your classmates.

*** IMPORTANT ***

Student submissions that contain work from another student or source will receive a zero for that assignment. A second occurrence will result in failing the class, and the academic sanction will be reported to the Dean of Students.

Generative AI Use Policy:

Use of Generative AI tools (such as ChatGPT, Copilot, Gemini, etc.) is permitted in this course, but only within strict boundaries that support learning without compromising academic integrity or the intent of the assignments.

Permitted Use:

  • Generative AI may be used to assist with understanding a specific concept, algorithm, or section of provided code.
  • Prompts must be narrowly focused (for example, explaining how an algorithm works or clarifying what a provided code segment does).
  • You may include code from the textbook, lecture materials, or other sources in a prompt only for the purpose of understanding it.
  • AI may be used to clarify or reinforce understanding, but not to generate code or complete any portion of an assignment.

Prohibited Use:

  • Do not prompt with entire assignments, assignment questions, or large portions of an assignment.
  • Do not use AI to generate code that is submitted as part of your work.
  • Do not use AI to debug, improve, or modify code that you are submitting.

Disclosure Requirement:

  • You must submit a copy of all AI prompts and corresponding responses along with your assignment, including coding assignments.
  • Submissions without accompanying AI usage documentation will be treated as not using AI.

Violations:

  • First violation: zero on the assignment.
  • Second violation or use of AI beyond what was disclosed may result in a formal academic integrity referral.

This policy is intended to support learning while ensuring that submitted work reflects your own understanding and effort.

Assignments (50%):

Individual problems and small programs worked outside of class.

Exams (20%):

Written exams taken in class.

Project (20%):

Individual or team project worked outside of class.

Participation (10%):

In class quizzes and labs, attendance, and overall participation.

Grading

GRADE GPA Range
 A  4.00 93 - 100
 A-  3.75 90 - 92
 B+ 3.25 87 -  89
 B  3.00 83 -  86
 B- 2.75 80 -  82
 C+ 2.25 77 -  79
 C  2.00 70 -  76
 D  1.00 60 -  69
 F  0.00  0 -  59

University Resources and Syllabus Policies