On-Device Machine Learning is a project-based course covering how to build, train, and deploy models that can run on low-power devices (e.g. smart phones, refrigerators, and mobile robots). The course will cover advances topics on distillation, quantization, weight imprinting, power calculation and more. Every week we will discuss a new research paper and area in this space one day, and have a lab working-group the second. Specifically, students will be provided with low-power compute hardware (e.g. SBCs and inference accelerators) in addition to sensors (e.g. microphones, cameras, and robotics) for their course project. The project will involve three components for building low-power multimodal models:
        (1) inference
        (2) performing training/updates for interactive ML, and
        (3) maximizing power.
The more that can be performed on device, the more privacy preserving and mobile the solution is.
For each stage of the course project, the final model produced will have an mAh "budget" equivalent to one full charge of a smart phone battery (~4 Ah or 2hrs on Jetson Nano, 7hrs on RPi, or 26hrs on a RPi Zero W).

  • Time & Place: 10:10am - 11:30am Tu/Th in POS 151
  • Course questions and discussion: Slack -- Registered students will be added
  • GitHub template for assignments: https://github.com/strubell/11-767
  • Assignments will be submitted and graded using Canvas
Example Industry Motivation "... if the coffee maker with voice recognition was in use for four years, the speech recognition cost for chewing on data back in the Mr Coffee datacenter would wipe out the entire revenue stream from that coffee maker, but that same function, if implemented on a device specifically tuned for this very precise job, could be done for under $1 and would not affect the purchase price significantly. " -- Source


Slack and Course Communication

All course communication will happen via slack including slides and discussions. Private correspondence with instructors should happen via email.


Policies and Grading

The course is split half on paper discussion and half projects.
Papers Project/Lab
– Participation 20% – Lab Reports 45%
– Paper Presentations 15% – Final Report & Presentation 20%
Participation in Class or Slack #hardware/#paper-discussion (20%)
Participation is evaluated as "actively asking/answering questions based on the lectures, readings, and/or assisting other teams with project issues". Concretely, this means that every novel question or helpful answer provided in Slack will count for 1%, up to a total of 20% of your grade.

Submission Policies:
COVID Details:
In the event a student tests positive for COVID-19, they will be invited to attend discussion virtually and will be expected to participate as usual. This includes participation points for raising their hands with questions/answers and submission of lab-notebooks. Note, that students who attend class while exhibiting symptoms will be told to leave and join virtually for the protection of all others present.

Accommodations for Students with Disabilities:
If you have a disability and have an accommodations letter from the Disability Resources office, we encourage you to discuss your accommodations and needs with the instructors as early in the semester as possible. We will work with you to ensure that accommodations are provided as appropriate. If you suspect that you may have a disability and would benefit from accommodations but are not yet registered with the Office of Disability Resources, we encourage you to contact them at access@andrew.cmu.edu.

Note to students

Take care of yourself! As a student, you may experience a range of challenges that can interfere with learning, such as strained relationships, increased anxiety, substance use, feeling down, difficulty concentrating and/or lack of motivation. This is normal, and all of us benefit from support during times of struggle. There are many helpful resources available on campus and an important part of a healthy life is learning how to ask for help. Asking for support sooner rather than later is almost always helpful. CMU services are available free to students, and treatment does work. You can learn more about confidential mental health services available on campus through Counseling and Psychological Services (CaPS). Support is always available (24/7) at: 412-268-2922.

Take care of your classmates and instructors! In this class, every individual will and must be treated with respect. The ways we are diverse are many and are fundamental to building and maintaining an equitable and an inclusive campus community. These include but are not limited to: race, color, national origin, caste, sex, disability (visible or invisible), age, sexual orientation, gender identity, religion, creed, ancestry, belief, veteran status, or genetic information.

Research shows that greater diversity across individuals leads to greater creativity in the group. We at CMU work to promote diversity, equity and inclusion not only because it is necessary for excellence and innovation, but because it is just. Therefore, while we are imperfect, we ask you all to fully commit to work, both inside and outside of our classrooms to increase our commitment to build and sustain a campus community that embraces these core values. It is the responsibility of each of us to create a safer and more inclusive environment. Incidents of bias or discrimination, whether intentional or unintentional in their occurrence, contribute to creating an unwelcoming environment for individuals and groups at the university. If you experience or observe unfair or hostile treatment on the basis of identity, we encourage you to speak out for justice and offer support in the moment and/or share your experience using the following resources:

Projects, Hardware, and Resources

The course will be primarily centered on a few multimodal tasks/platforms to facilitate cross-team collaboration and technical assistance. If your team wants to use custom hardware or sensors not listed here -- that's fine, but please reach out so we can discuss it and put think through the implications. Every team will also be provided with one of the following Single Board Computers (SBCs)

Example Projects
Input Output Task
Speech Text Open-Domain QA
Images Text Object Detection or ASL Finger Spelling
Images Robot Arm Learning from Demonstration
Speech + Images Robot Car Vision-Language Navigation
Side view of three-wheel robot car LCD Screen and Camera for ASL
Single Board Computers
Raspberry Pi 4 8GB 2Amp draw on moderately powerful processor
Jetson Nano 2GB, 4GB 128-core NVIDIA Maxwell CUDA cores
  1. Can we use other platforms? Yes! But let's talk about the pros and cons together.
  2. What about custom sensors and hardware? Same answer :)
  3. Will the course pay for a Coral/Pi Zero/...? Yes, but let's chat first.

Course Schedule

A basic course schedule is presented below. Papers will be chosen jointly by the instructors and the student presenting.
Aug 31: Course structure & Background
Sept 2: Hardware landscape and trade-offs
Sept 7: Intro to practical applications Sept 9: Hardware setup & run inference
  • Lab Report 0 practice
Sept 14: Compression I: Distillation & Pruning Sept 16: Project expectations and brainstorming
  • Lab Report 1 5%
Sept 21 Project Proposal Workshopping (Lab)
  • Project Proposal Due
Sept 23: Neural Architecture Search (Discussion)
Sept 28: Benchmarking
Sept 30: Benchmarking
  • Lab Report 2 5%
Oct 5: Compression II: Quantization Oct 7: Quantization
  • Lab Report 3 5%
Oct 12: Multimodal Fusion Oct 14: No class: Semester Break
Oct 19: Architecture specific tricks I: CNNs Oct 21: Implement baselines and related work
  • Lab Report 4 5%
Oct 26: Architecture specific tricks II: Transformers Oct 28: Group Work
  • Lab Report 5 5%
Nov 2: Guest lecture: Shinji Watanabe Nov 4: Group Work & Project Meetings
Nov 9: Accelerating Training Nov 11: Group Work & Project Meetings
  • Lab Report 7 5%
Nov 16: Paper Presentations I Nov 18: Group Work
  • Lab Report 8 5%
Nov 23: Paper Presentations II Nov 25: No class: Thanksgiving
Nov 30: Carbon and the Future Dec 2: Carbon calculation and final report/presentation prep
  • Lab Report 9 5%
Dec 9: Final Presentation (5:30-8:30pm in MM A14) Dec 10: Final Report due

Example Readings