Han-Japan-1.29.2021

• Video details

  Deep learning is computation-hungry and data-hungry. We aim to improve the computation efficiency and data efficiency of deep learning. I will first talk about MCUNet that brings deep learning to IoT devices. The technique is tiny neural architecture search (TinyNAS) co-designed with a tiny inference engine (TinyEngine), enabling ImageNet-scale inference on an IoT device with only 1MB of FLASH. Next I will talk about TinyTL that enables on-device transfer learning, reducing the memory footprint by 7-13x.  Finally, I will describe Differentiable Augmentation that enables data-efficient GAN training, generating photo-realistic images using only 100 images, which used to require tens of thousand. We hope such TinyML techniques can make AI greener, faster, and more sustainable.

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