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Event track: AI in the Real World: From Development to Deployment

Featured: AI solutions in emerging markets - Highlighting African use-cases

Presented by: Ahmad Batour, Google | Peter Warden, Tensorflow | Stephen Ozoigbo, Arm

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Next up on our schedule:

Date: January 11, 2022
Time: 8am PT / 4pm GMT / 11pm Shanghai
Will Levine, Senior Machine Learning Engineer @ Qeexo

Qeexo AutoML is an end-to-end framework for developing machine learning solutions for sensor-enabled embedded devices, covering all steps from data collection to deployment. Both Arm Cortex-M4F and Arm Cortex-M0+ are supported as target platforms. Though the M0+ platform lacks a floating-point unit and is typically limited in RAM, Qeexo’s highly-optimized featurization pipeline makes it a great choice for building full machine learning solutions with minimal power consumption and minimal hardware cost. The M4F platform is capable of heavier tasks, involving high-data-rate sensors and neural networks.

This talk will cover:

  • A demonstration using the Qeexo AutoML platform to build an activity recognition solution for the M0+-powered Arduino Nano 33 IOT
  • The technology that powers Qeexo’s machine learning for M0+ platforms
  • A discussion of appropriate machine-learning applications for M0+ and M4F platforms
  • Description of how these approaches can be extended to future Arm platforms through the Arm Virtual Hardware Platform

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Arm DevSummit 2020

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