Have you found it difficult to deploy neural networks on mobile devices and IoT devices? Have you ever found it too slow to train neural networks? This course is a deep dive into efficient machine learning techniques that enable powerful deep learning applications on resource-constrained devices. Topics cover efficient inference techniques, including model compression, pruning, quantization, neural architecture search, and distillation; and efficient training techniques, including gradient compression and on-device transfer learning; followed by application-specific model optimization techniques for videos, point cloud, and NLP; and efficient quantum machine learning. Students will get hands-on experience implementing deep learning applications on microcontrollers, mobile phones, and quantum machines with an open-ended design project related to mobile AI.
- Time: Tuesday/Thursday 3:30-5:00 pm Eastern Time
- Location: 36-156
- Office Hour: Thursday 5:00-6:00 pm Eastern Time, 38-344 Meeting Room
- Discussion: Piazza
- Homework submission: Canvas
- Online lectures: The lectures will be streamed on YouTube.
- Resources: MIT HAN Lab, Github, TinyML, MCUNet, OFA
- Contact: Students should ask all course-related questions on Piazza. For external inquiries, personal matters, or emergencies, you can email us at [email protected].