MCUXpresso_MIMXRT1052xxxxB/boards/evkbimxrt1050/eiq_examples/glow_lenet_mnist

Overview

This example project provides an inference example using the Lenet model compiled with the Glow AOT software tools. The model is capable to perform hand-written digit classification. The model is using 28 x 28 grayscale input images and provides the confidence scores for the 10 output classes: digits "0" to "9". The application will run the inference on a sample image and display the top1 classification results and the inference time. The project example will walk through all the steps of downloading and compiling the model, pre-processing a sample image, building and running the project.

Files: main.c - example source code timer.c - implementation of helper functions for measuring inference time timer.h - declarations of helper functions for measuring inference time

Toolchains supported

• MCUXpresso IDE
• IAR Embedded Workbench for ARM
• Keil uVision MDK
• ArmGCC - GNU Tools ARM Embedded

SDK version

• Version: 2.15.000

Toolchain supported

• IAR embedded Workbench 9.40.1
• Keil MDK 5.38.1
• GCC ARM Embedded 12.2
• MCUXpresso 11.8.0

Hardware requirements

• Mini/micro USB cable
• EVKB-IMXRT1050 board
• Personal Computer

Board settings

No special settings are required.

Prepare the Demo

1. Connect a USB cable between the host PC and the OpenSDA USB port on the target board.
2. Open a serial terminal with the following settings:
   • 115200 baud rate
   • 8 data bits
   • No parity
   • One stop bit
   • No flow control
3. Download the program to the target board.
4. Either press the reset button on your board or launch the debugger in your IDE to begin running the demo.

Running the demo

The log below shows the output of the Release version in the terminal window:

Top1 class = 9 Confidence = 0.942 Inference time = 10 (ms)

Notes The inference time depends on the board. For example you can expect the following inference time for the following boards:

RT10xx: Inference time: 10 (ms) RT11xx: Inference time: 6 (ms)