MCUXpresso_MIMXRT1052xxxxB/boards/evkbimxrt1050/eiq_examples/glow_cifar10

Overview

Demonstrates inference for models compiled using the GLOW AOT tool. The network used in this is based on the CIFAR-10 example in Caffe2 [1] & [2].

[1] https://github.com/caffe2/tutorials/blob/master/CIFAR10_Part1.ipynb [2] https://github.com/caffe2/tutorials/blob/master/CIFAR10_Part2.ipynb

This project does not include the pre-trained model or the training script since Caffe2 framework is deprecated and lately has become part of PyTorch. This project example only includes the bundle (binary) generated after running the Glow AOT tool and is intended to be used as-is. If you want a step-by-step example of running the Glow AOT tool for a given model take a look at the LeNet MNIST Glow example.

The neural network consists of 3 convolution layers interspersed by ReLU activation and max pooling layers, followed by a fully-connected layer at the end. The input to the network is a 32x32 pixel color image, which will be classified into one of the 10 output classes.

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 = 8 (ship) Confidence = 0.728 Inference time = 24 (ms)