Class Syllabus

The general format of the course consists of some regular lectures, problem sets, labs, a project, and exams.

Grading Policy

• Grades will be posted on Canvas.
• Participation: 10%
• Midterm Exam: 20%
• Laboratory-based grading track:
  • Laboratory Assignments: 55%
  • Final Exam: 15%
• Project-based grading track:
  • Laboratory Assignments: 40%
  • Major Class Project: 30%
• Grades will be assigned two ways:
  • >90 [A], >80 [B], >70 [C], >60 [D]
  • Curved according to the class median. Here your relative score is what really matters, rather than the objective scores above.

Missed Class Policy

Students must contact the instructor prior to missing an exam to schedule a makeup exam. Makeup exams will only be scheduled following an exam in cases of documented emergencies.

The ongoing global pandemic has been very disruptive and the situation is still highly fluid. We understand and will be very accomodating. Please note that any COVID-19 accommodations that you may need throughout the course of the semester are best discussed with medical documentation.

Late Submission Policy

Problem sets or laboratory assignments or course project submitted after the due date/time are considered late and will not be graded. Late submissions will only be graded in cases of documented emergencies.

Plagiarism

Discussion of course material and collaboration with other students is encouraged but each student must write/type and submit his/her own solution. Your essays, code and proofs (if applicable) should never contain sections which are identical to the submission of another student, past or present. Submitted work must be entirely that of the student(s) whose name(s) appear(s) on the submission and not solicited in any way from others. Violation of these policies can result in automatic failure of the course.

Please refer to the Arizona State University’s policy on academic dishonesty.

Tentative Schedule

Monday	Wednesday
Jan 10 - Review Module - LR-1 / LR-5 Computer Organization Review Self-Assessment M. Kinsy	Jan 12 - Learning Module 1 - L01 Influence of Technology and Software on Instruction Sets Self-Assessment DUE Laboratory 0 M. Kinsy
Jan 17 Martin Luther King Jr. Holiday M. Kinsy	Jan 19 - Learning Module 1 - L02 Intel Pin introduction Laboratory 0 DUE Laboratory 1 M. Kinsy
Jan 24 - Learning Module 2 - L03 Single-cycle ISA Implementation M. Kinsy	Jan 26 - Learning Module 2 - L04 CPU Pipelining M. Kinsy
Jan 31 - Learning Module 2 - L05 Structural, Data and Control Hazards M. Kinsy	Feb 02 - Learning Module 2 - L06 Hazard Resolution M. Kinsy
Feb 07 - Learning Module 2 - L07 Performance Analysis M. Kinsy	Feb 09 - Learning Module 3 - L08 Branch Prediction Laboratory 1 DUE Laboratory 2 M. Kinsy
Feb 14 - L09 Complex Pipelining: Superscalar M. Kinsy	Feb 16 - L10 Multithreading M. Kinsy
Feb 21 - E01 Midterm Exam	Feb 23 - L11 Memory Organization I M. Kinsy
Feb 28 - L12 Memory Organization II M. Kinsy	Mar 02 - L13 Advanced Memory Operations Laboratory 2 DUE Laboratory 3 M. Kinsy
Mar 07 Spring Recess	Mar 09 Spring Recess
Mar 14 - L14 Process Synchronization I M. Kinsy	Mar 16 - L15 Process Synchronization II M. Kinsy
Mar 21 - L16 Sequential Consistency M. Kinsy	Mar 23 - L17 Cache Coherence - Snoopy Cache Laboratory 3 DUE Laboratory 4/Project M. Kinsy
Mar 28 - L18 Cache Coherence - Directory Cache M. Kinsy	Mar 30 - L19 On-chip Network Architectures M. Kinsy
Apr 04 - L20 On-chip Routing M. Kinsy	Apr 06 - L21 Complex Pipelining: VLIW Laboratory 4 DUE Laboratory 5 Mid-Project Status M. Kinsy
Apr 11 - L22 SIMD and Vector Processors M. Kinsy	Apr 13 - L23 GPU Architectures M. Kinsy
Apr 18 - L24 Lecture/Review M. Kinsy	Apr 20 Lecture/Review Laboratory 5 DUE M. Kinsy
Apr 25 Lecture/Review M. Kinsy	Apr 27 Lecture/Review Project DUE M. Kinsy
May 02 Final examinations	May 05 Final examinations