Exploring Edge AI with NXP's FRDM-MCXN947

I recently attended NXP technology day in Auckland, where the latest innovations from NXP were showcased - including their microcontrollers, i.MX microprocessors, and other advanced semiconductor technologies. NXP, a leading Dutch semiconductor company, designs and manufactures a wide array of products for automotive, industrial, and IoT applications. Among their latest developments is the MCX series of microcontrollers, which unifies the legacy Philips and Kinetis families into a new, modern MCU architecture.

What particularly caught my attention was the MCXN947 from the MCX N series. This device delivers high performance and low power consumption, while integrating a neural processing unit (NPU) to enable on-device AI processing. As computing increasingly moves to the edge, it's exciting to see compact, energy-efficient hardware like this supporting real-time AI workloads. NXP also hosted a hands-on machine learning session with the MCXN947, which inspired me to explore its potential as an edge AI platform.

• Hardware Requirements
• Software Setup
• Building a Classification Project
• Deploying the Model
• MCUXpresso IDE
• Data Logging
• Anomaly Detection

Getting Started with FRDM-K82F

FRDM-K82F is a low cost development platform using MK82FN256VLL15 microcontroller. When I started using the board according to its getting started guide at

Get Started with the FRDM-K82F [NXP16],

I found out that its out-of-box demo is not running on my version of FRDM-K82F board. The insturctions on that page are also out of date. And, I could not get it worked.

After spending my time testing, browing the Internet, and experimenting, I found out that the following things might be necessary, in a brief, to make the board worked.

• Debug adapter - update it
• IDE - use MCUXpresso instead of Kinetis Studio
• SDK - use MCUXpresso SDK Builder (make sure to 'drag and drop')
• Flash - unlock the MCU's flash if required

Figure. FRDM-K82F board.