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=Introduction=
This Technical Note (TN) describes a demo application used to show the combination of an inference algorithm (, namely keyword spotting) , and an asymmetric multiprocessing configuration scheme (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 Executing 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 system such as Linux might not be suited either because unable to handle tight real-time constrained taskedtasks.
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 this heterogeneous architecture to implement an AMP configuration where * The Cortex-A53 complex — running Yocto Linux — is devoted to execute the inference algorithm with the hardware acceleration provided by the NPU * The Cortex-M7 core takes care of data acquisition from sensors.
=Testbed=
The testbed is illustrated in the following picture. Basically, it consists of an Orca SBC
=Implementation=
=Testing=
