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Putting AI on a Diet: TinyML and Efficient Deep Learning

tinyML via YouTube

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This talk presents techniques for efficient deep learning on resource-constrained devices, including model compression, neural architecture search, accelerator design, and TinyML applications in vision, point clouds, and language.

Syllabus

Intro
Today's Al is too Big
Deep Compression
Pruning & Sparsity
Once-for-All Network: Roofline Analysis
OFA Designs Light-weight Model, Bring Alto Mobile Devices
NAAS: Neural Accelerator Architecture Search
Application Specific Optimizations
TinyML for Video Recognition
TinyML for Point Cloud & LIDAR Processing
SpAtten: Sparse Attention Accelerator
TinyML for Natural Language Processing
Tiny Transfer Learning

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tinyML

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