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Building Edge Applications on Microcontrollers

  • Cheng-Fu Tan, Arm

Many have believed that machine learning was computationally infeasible on microcontrollers (MCUs). Recent works in algorithm and software engineering have reduced the computational requirement for deep learning significantly. This enables deep learning models to be deployed on affordable and simple systems, the MCUs. Intelligence on the edge will allow us to build smarter devices and serve new applications. In this talk, we will look at how to train a simple model in Tensorflow, then use uTensor and CMSIS-NN to deploy it on Mbed.

  • Date:Thursday, October 18
  • Time:4:30 PM - 5:20 PM
  • Location:Executive Ballroom 210G
  • Session Type:Conference Session
  • Room:Executive Ballroom 210G
  • Pass Type:All-Access Pass
  • Secondary Track:Embedded Software Development
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