ML-TN-006 — Keyword Spotting and Asymmetric Multiprocessing on Orca SBC

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Revision as of 16:35, 6 December 2021 by U0001 (talk | contribs) (Introduction)
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NeuralNetwork.png Applies to Machine Learning


History[edit | edit source]

Version Date Notes
1.0.0 December 2021 First public release

Introduction[edit | edit source]

This Technical Note (TN) describes a demo application used to show the combination of an inference algorithm (keyword spotting) and an asymmetric multiprocessing configuration (AMP). This use case can serve as the basis for more complex applications that have to carry out the following tasks:

  • Acquiring data from sensors in real-time
  • Perform a computationally expensive inference algorithm on the collected data.

This scenario is quite common in the realm of AI at the edge and generally it can not be addressed with a microcontroller-based solution. On the other hand, a classic embedded processor running a complex operating systems such as Linux might not be suited either because unable to handle tight real-time constrained tasked.

In such cases, the power and the flexibility of the NXP i.MX8M Plus can be of much help, as this SoC features a heterogeneous architecture — an ARM Cortex-A53 complex and an ARM Cortex-M7 core — and a Neural Processing Unit (NPU).

The idea is to exploit the heterogeneous architecture to implement an AMP configuration where

  • The Cortex-A53 complex running Yocto Linux is devoted to execute the inference algorithm
  • The Cortex-M7 takes care of data acquisition from sensors.

Testbed[edit | edit source]

Implementation[edit | edit source]

Testing[edit | edit source]