Fall 2019 - CMPT 982 G100

Special Topics in Networks and Systems (3)

Machine Learning

Class Number: 10715

Delivery Method: In Person


  • Course Times + Location:

    Mo, We, Fr 2:30 PM – 3:20 PM
    AQ 5016, Burnaby

  • Exam Times + Location:

    Dec 14, 2019
    12:00 PM – 3:00 PM
    AQ 5030, Burnaby



This course will explore, from a computer architecture perspective, the principles of hardware/software codesign for machine learning. One thrust of the course will delve into accelerator, CPU, and GPU enhancements for ML algorithms, including parallelization techniques. The other thrust of the course will focus on how machine learning can be used to optimize conventional architectures by dynamically learning and adapting to program behavior. Is this a machine learning course? ------------------------------------ Not really – the computation behind machine learning and how that is exploited with hardware is what is most relevant here. Prerequisites -------------- It is recommended that you have taken some courses in computer organization. Expected background includes basic knowledge of simple hardware pipelines (ie. how does an inorder processor work?). Strong programming background: C++/C All of that said, we will spend time going in depth on background/review during the first two-or-so weeks to build a foundation for more advanced architecture concepts.
Projects and Assignments -------------------------- We will be working with FPGAs, designing hardware targetting specific algorithms.


  • Machine Learning
  • Hardware architecture
  • Hardware/Software Co-design



Project - 50% , Assignments - 50%

Graduate Studies Notes:

Important dates and deadlines for graduate students are found here: http://www.sfu.ca/dean-gradstudies/current/important_dates/guidelines.html. The deadline to drop a course with a 100% refund is the end of week 2. The deadline to drop with no notation on your transcript is the end of week 3.

Registrar Notes:

SFU’s Academic Integrity web site http://www.sfu.ca/students/academicintegrity.html is filled with information on what is meant by academic dishonesty, where you can find resources to help with your studies and the consequences of cheating.  Check out the site for more information and videos that help explain the issues in plain English.

Each student is responsible for his or her conduct as it affects the University community.  Academic dishonesty, in whatever form, is ultimately destructive of the values of the University. Furthermore, it is unfair and discouraging to the majority of students who pursue their studies honestly. Scholarly integrity is required of all members of the University. http://www.sfu.ca/policies/gazette/student/s10-01.html