
This SDK has been patched by Embedded Artists for the iMXRT1062 Developer's Kit.
The SDK was released on 2022-11-18 and is based on NXP's 2.12.1 SDK (SDK_2_12_1_MIMXRT1062xxxxA.zip).

This is what has been patched:
* Set CPU speed according to Commercial/Industrial CPU
* Correction of the VDD_SOC_IN voltage.
* Flash settings (speed, algorithm, size, driver) to work with the 4MB OctalSPI ATXP032
* LWIP projects - added reading of the MAC address from the onboard I2C EEPROM
* Added an I2C driver for the gpio expander (PCA6416) and code to use it
* Modified pin muxing
* SEMC projects - changed algorithm for memory test and now test entire 32MB instead of only 4KB
* Adjusted the USB interface number for USB Host examples (it is different for host and device examples)
* Added a software_reset() function in board.c/.h to issue a JEDEC reset before NVIC_SystemReset()
* Changed the Wi-Fi examples to use the Embedded Artists 1XK M.2 Module (EAR00385) as default
* Many of the projects have been updated to use a more complete pin_mux.c file where all
  necessary pins have been initialized. The SDK examples used to only configure the pins
  that they use (and often not every pin) and most of the time the configuration was only
  for MUX:ing and not the PAD settings (pull up/down/none, drive strength and slew).
* Embedded Wizard project 'ew_gui_smart_thermostat' was incorrectly setup for EVKB
* Changed the default display to RK043FN02H as it is the one mounted on the Developer's Kits

This has been added:
* New WDOG examples that work
* I2C probe example
* Example to show the use of software_reset()

This has been removed:
* All projects for the EVK - only keeping EVKB which is then patched
* The original WDOG and RTWDOG examples as those were not working

Important things to note:
* Read section "8 - Known Issues" in docs/MCUXpresso SDK Release Notes for EVK-MIMXRT1060.pdf
  to see known issues with the current version of the SDK.
* For Iperf examples, set compiler optimization to -O3 or similar to improve performance.
* If the hardware seems unresponsive and the debugger cannot connect/flash/erase the current program
  then the most likely cause is the running program preventing the access. To stop the currently
  running program and regain control:
  1) Press and hold down the ISP_ENABLE button (SW1)
  2) Press and hold down the RESET button (SW3)
  3) Let go of the RESET button
  4) Wait an extra second or two
  5) Release the ISP_ENABLE button
  6) The hardware is now in a mode where programming/erasing it should work

Connectors:
* J22 (micro USB) is the default UART and unless specified otherwise it is setup for 115200 8/N/1


Everything below this line is the original content of the readme file.
=======================================================================



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

Hardware requirements
=====================
- Mini/micro USB cable
- EVK-IMXRT10xx, EVK-IMXRT11xx or EVKB-IMXRT1050 board
- Personal computer (Windows)
- Python 3.x installed
- Glow AOT tools installed for Windows and added to system PATH variable:
  - image-classifier.exe
  - model-compiler.exe

You can find the Glow Windows Installer on the NXP eIQ mirror:
https://www.nxp.com/design/software/development-software/eiq-ml-development-environment:EIQ

Board settings
==============
No special settings are required.

Prepare the demo
================
0. Create a folder "model" in the root directory of the project with two subfolders:
   "lenet_mnist" and "dataset".

1. Download the Lenet model files (Caffe2 format) using your browser from the
   following links into the "model\lenet_mnist" project directory:

   http://fb-glow-assets.s3.amazonaws.com/models/lenet_mnist/predict_net.pb
   http://fb-glow-assets.s3.amazonaws.com/models/lenet_mnist/init_net.pb

2. Download the Mnist image samples using your browser from the official Glow
   repository into the "model\dataset" project directory. The images contain
   one sample image for each digit from "0" to "9".

   https://github.com/pytorch/glow/blob/master/tests/images/mnist/0_1009.png?raw=true
   https://github.com/pytorch/glow/blob/master/tests/images/mnist/1_1008.png?raw=true
   https://github.com/pytorch/glow/blob/master/tests/images/mnist/2_1065.png?raw=true
   https://github.com/pytorch/glow/blob/master/tests/images/mnist/3_1020.png?raw=true
   https://github.com/pytorch/glow/blob/master/tests/images/mnist/4_1059.png?raw=true
   https://github.com/pytorch/glow/blob/master/tests/images/mnist/5_1087.png?raw=true
   https://github.com/pytorch/glow/blob/master/tests/images/mnist/6_1099.png?raw=true
   https://github.com/pytorch/glow/blob/master/tests/images/mnist/7_1055.png?raw=true
   https://github.com/pytorch/glow/blob/master/tests/images/mnist/8_1026.png?raw=true
   https://github.com/pytorch/glow/blob/master/tests/images/mnist/9_1088.png?raw=true

3. Perform profiling on the Mnist dataset using the "image-classifier" Glow tool.
   Open a Windows Command Prompt with the current path set within the project 
   directory and run the following command:

image-classifier model\dataset\*.png ^
    -image-mode=0to1 -image-layout=NCHW ^
    -model=model\lenet_mnist -model-input-name=data ^
    -dump-profile=model\profile.yml ^
    -quantization-schema=symmetric_with_power2_scale ^
    -quantization-precision-bias=Int8

4. Compile the model and generate the bundle using the "model-compiler" Glow tool.
   Run the following command:

model-compiler -model=model\lenet_mnist -model-input=data,float,[1,1,28,28] ^
    -backend=CPU -target=arm -mcpu=cortex-m7 -float-abi=hard ^
    -emit-bundle=source -load-profile=model\profile.yml ^
    -quantization-schema=symmetric_with_power2_scale ^
    -quantization-precision-bias=Int8 ^
    -use-cmsis

5. Use the python script "scripts\glow_process_image.py" to transform an input
   image into a C array data and save it as "source\input_image.inc" which is
   included in main.cpp. As an example we will use the sample image "9_1088.png"
   which contains the digit "9":

python scripts\glow_process_image.py ^
    -input-path=model\dataset\9_1088.png ^
    -output-path=source\input_image.inc ^
    -image-mode=0to1 -image-layout=NCHW -image-channel-order=RGB

6. Import the project in IDE and link the Glow bundle by performing the following steps:

   For MCUXpresso IDE:
     - Right click on the project -> "Properties" 
     - Select "C/C++ Build" -> "Settings" 
     - In the "Tool Setting" tab select "MCU C++ Linker" -> "Miscellaneous" 
     - Add the bundle to "Other objects" panel by clicking "Add..." and specify
       the relative path to the bundle in the project: "../source/lenet_mnist.o"

   For IAR Embedded Workbench:
     - Right click on the "source" folder -> Add -> Add Files...
     - Choose file category "Library/Object Files (*.r;*.a;*.lib;*.o)"
     - Select the object file "lenet_mnist.o" located in "source" folder

   For Keil uVision MDK:
     - Right click on the target -> "Options for Target ..."
     - Select "Linker" tab
     - In the "Misc controls" section add to the existing string with a 
       separating space (" ") character the string "../source/lenet_mnist.o"

   For Armgcc:
     - Edit the "CMakeLists.txt" file from the "armgcc" directory by adding the
       following line at the end of the file:
         target_link_libraries(glow_lenet_mnist.elf ${ProjDirPath}/../source/lenet_mnist.o)

7. Build the project.

8. Connect a USB cable between the host PC and the OpenSDA USB port on the target board.

9. Open a serial terminal with the following settings:
   - 115200 baud rate
   - 8 data bits
   - No parity
   - One stop bit
   - No flow control

10. Download the program to the target board.

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)