The Digi ConnectCore® 93 Development Kit (DVK) and System-on-Module (SOM) platform is a highly integrated, cost-effective, connected, secure embedded solution, built on the i.MX 93 MPU family. It integrates memory, power management, pre-certified wireless connectivity, and advanced Digi TrustFence device security with a complete, open-source Linux software platform based on the Yocto Project.
The i.MX 93 applications processors are the first in the i.MX portfolio to integrate the scalable Arm Cortex-A55 core, bringing performance and energy efficiency to Linux®-based edge applications and the Arm Ethos™-U65 microNPU, enabling developers to create more capable, cost-effective and energy-efficient ML applications.
Optimizing performance and power efficiency for Industrial, IoT and automotive devices, i.MX 93 processors are built with NXPs innovative Energy Flex architecture. The SoCs offer a rich set of peripherals targeting automotive, industrial and consumer IoT market segments.
Part of the EdgeVerse™ portfolio of intelligent edge solutions, the i.MX 93 family will be offered in commercial, industrial, extended industrial and automotive level qualification and backed by NXPs product longevity program.
Key Features:
1-2x Arm® Cortex®-A55 @ 1.7 GHz
Arm Cortex-M33 @ 250Mhz
Arm® Ethos™ U-65 microNPU
EdgeLock® secure enclave
Up to 1 GB, 16-bit LPDDR4 memory
Up to 8 GB, 8-bit eMMC memory
IEEE 802.11 a/b/g/n/ac/ax WLAN and Bluetooth 5.3
ConnectCore 93 Development Kit PCBA
5V/3A power supply with EU, UK, AUST adapters
USB type-C cable
Whip antenna, Wi-Fi 2.4/5GHz
In addition to the DVK we recommend that you also add a camera. Most popular USB webcams work fine on the development board out of the box.
A few steps need to be performed to get your board ready for use.
You will also need the following equipment to complete your first boot.
Ethernet cable
Digi provides a ready-made operating system based on Yocto Linux, which can be downloaded from their Getting Started guide here.
Step 3 includes instructions for flashing the device, use the UUU method as the SD card path is currently untested with Edge Impulse.
If you encounter problems obtaining the images, this link has been tested and works as of July 2024.
In step 5 of the UUU instructions: Connect a USB type-C cable to your development PC and the other end to the target USB type-C connector. Is referring to J63 type-C connector near the type-A USB ports.
Once booted up, connect a terminal to the device over USB or preferably SSH, and run the following commands:
You may need to reboot the board once the dependencies have finished installing. Once rebooted, run:
This will start a wizard which will ask you to log in, and choose an Edge Impulse project. If you want to switch projects run the command with --clean.
That's all! Your Digi i.MX 93 DVK is now connected to Edge Impulse. To verify this, go to your Edge Impulse project, and click Devices. The device will be listed here.
With everything set up you can now build your first machine learning model with this tutorial:
To run your impulse locally on the i.MX 93 DVK, open up a terminal and run:
This will automatically compile your model, download the model to your i.MX 93 DVK, and then start classifying. Our Linux SDK has examples on how to integrate the model with your favourite programming language.
If you have an image model then you can get a peek of what your i.MX 93 DVK sees by being on the same network as your device, and finding the 'Want to see a feed of the camera and live classification in your browser' message in the console. Open the URL in a browser and both the camera feed and the classification are shown:
The i.MX 93 DVK is a fully-featured development kit, making it a great option for machine learning on the edge. With its Digi Embedded Yocto (DEY) Linux OS flashed, it is capable of both collecting data, as well as running local inference with Edge Impulse.
If you have any questions, be sure to reach out to us on our Forums!
