MIT Subject Listing & Schedule
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6.5940 TinyML and Efficient Deep Learning Computing

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Prereq: 6.1910 and 6.3900
Units: 3-0-9

Introduces efficient deep learning computing techniques that enable powerful deep learning applications on resource-constrained devices. Topics include model compression, pruning, quantization, neural architecture search, distributed training, data/model parallellism, gradient compression, on-device fine-tuning. It also introduces application-specific acceleration techniques for video recognition, point cloud, and generative AI (diffusion model, LLM). Students will get hands-on experience accelerating deep learning applications with an open-ended design project.
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