Logistics

• Textbook and Design Tool
• Prerequisites
• Assignments, Tests and Final Project
• Grading
• Student Code of Conduct

Textbook and Design Tool

• Recommended readings:
  • Overview paper on DNN acceleration
  • Deep Learning (Adaptive Computation and Machine Learning series)
  • Deep Learning: A Practitioner’s Approach, 1st Edition, By Josh Patterson and Adam Gibson
  • Deep Learning for Computer Architects, by Paul Whatmough, Gu-Yeon Wei, David Brooks

Prerequisites

• Undergraduate degree in Electrical Engineering, Computer Engineering or Computer Science.
• Familiarity with Python programming language and Deep Learning libraries such as Tensorflow, Caffe, Keras, Pytorch, etc.

Assignments, Tests and Final Project

• Each student will present one paper from the list provided on Canvas. Students will present in groups of two. The presentation sessions will be listed in the schedule.
• The papers to select for presentation will be listed soon.
• We will have a few lab homework assignments as indicated on the syllabus. The deadline for the assignment submission is 11:59PM on the due date.
• There is one final class project that covers a spectrum of basic deep learning to advanced optimization schemes for efficient DL.

Grading

The final grade breakdown is as follows:

• Class Presentation: 10%
• Assignments: 40% (due date on the schedule)
• Final Project: 50% (15% presentation due on 6/8/23 and 35% report due on 6/16/23)

Student Code of Conduct

• Adherence to UCSD student code of conduct is expected in all phases of this course
• Violations will be reported to the Student Conduct Office