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Unlocking AI on Arm Microcontrollers with Deep Learning Model Optimization
community.arm.com, Apr. 21, 2020 –
*** All content in this blog provided by Davis Sawyer, Co-founder & CPO at Deeplite.ai ***
The emergence of AI and deep learning on embedded devices and platforms has created opportunities for exciting new ways to make products more intelligent. In domains such as computer vision and natural language, deep neural networks (DNNs) have become the de facto tool for performing complex tasks; even outperforming humans at recognizing objects in images. Therefore, DNNs have become much more complicated and computationally demanding in recent years, performing ever-more interesting and intelligent use cases such as semantic segmentation and facial recognition. This has rendered many state-of-the-art model architectures impractical for everyday devices. For the billions of microcontrollers (MCUs) currently in use, this is ultimately preventing people from using AI on their devices.
It is no secret that deep learning has a size problem. For example, MegatronLM, a massive transformer model for language tasks weighs in at over 8 billion parameters (that is 33GB of memory), and requires 500 V100 GPUs over 9 days to train. Although an extreme example, the resource demands for most modern DNN models simply cannot run on the low-power computing hardware that is prevalent all around us in edge devices such as phones, cars, and sensors.
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