Have you found it difficult to deploy neural networks on resource-constrained hardware? 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 distributed training, gradient compression and on-device transfer learning; followed by application-specific model optimization techniques for video, point cloud, generative model, NLP and LLM; it will cover futuristic research on quantum machine learning. Students will get hands-on experience implementing deep learning applications on mobile devices with an open-ended design project related to efficient AI computing.

  • 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, 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].