Bureaucrats, dave_user, Administrators
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* Executing a computationally expensive inference algorithm on the collected data.
This scenario is quite common in the realm of AI at the edge and but, generally it , can not be addressed with a microcontroller-based solutionbecause it would take too long to run the inference algorithm. On the other hand, a classic embedded processor running a complex operating system such as Linux might not be suited either because unable to handle tight real-time constrained tasksproperly.
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 this i.MX8M Plus' heterogeneous architecture to implement an AMP configuration where * The Cortex-A53 complex — running Yocto Linux — is devoted to execute the inference algorithm with by leveraging the NPU hardware acceleration provided by the NPU * The Cortex-M7 core takes care of data acquisition.
=Testbed=
The testbed is illustrated in the following picture. Basically, it consists of an [[ORCA_SBC|Orca Single Board Computer]]
As stated previously, the inference algorithm is keyword spotting. The data being processed are thus audio samples retrieved by the Cortex M7 and sent to the Cortex A53 complex.
From a software perspective, we identify two different domains:
* D1, which refers to the Yocto Linux world running on the Cortex A53 complex
* D2, which refers to the firmware running on the Cortex M7 core.
For the sake of simplicity, the audio samples are not captured by a real microphone. They are retrieved by prefilled memory buffers that can not be accessed by Cortex A53 cores. For the purposes of discussion, this simplification is neglectable as the communication mechanisms between the domains are not affected at all.
=Implementation=
From
=Testing=
==Boot sequence==
This example was arranged in order to execute the following boot sequence:
