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Developing Machine Learning Solutions for the IoT Edge

  • Raviraj Mahatme, Arm

Machine learning, especially deep learning, based algorithms are gaining popularity in IoT edge devices, as they can offer human-level accuracy in many tasks, such as image classification and speech recognition. We have seen increasing interests in developing and deploying neural networks (NNs) on the types of low-power processors found in always-on IoT Edge systems, such as those based on Arm Cortex-M microcontrollers.

In this talk, we first discuss the challenges of deploying neural networks on microcontrollers with limited memory and compute resources and power budgets. We introduce CMSIS-NN, a library of optimized software kernels to enable deployment of neural networks on Arm Cortex-M processors. We also present techniques for NN algorithm exploration to develop lightweight models suitable for resource constrained systems.

  • Date:Wednesday, October 17
  • Time:2:30 PM - 3:20 PM
  • Location:Executive Ballroom 210E
  • Session Type:Conference Session
  • Room:Executive Ballroom 210E
  • Pass Type:All-Access Pass
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