
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


!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
THIS PROJECT IS NOT DIRECTLY COMPATIBLE WITH THE HARDWARE AND WILL NOT WORK.

This example for the EVKB requires requires a camera but that is not
supported on the iMX OEM Carrier Board.
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!



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



Overview
========
Demonstrates inference for models compiled using the GLOW AOT tool and uses
a camera to generate data for inferencing.

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 data captured by 
a connected camera and display the top1 classification results and the inference time. 
The Glow bundle is the same as the "glow_lenet_mnist" MCUXpresso SDK example in the RT1060 SDK.

A PDF with example numbers for the camera to look at is included in the /doc folder.

 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 MCUXpresso SDK example and the Glow Getting Started Lab:
 https://community.nxp.com/t5/eIQ-Machine-Learning-Software/eIQ-Glow-Lab-for-i-MX-RT/ta-p/1123119


Toolchains supported
====================
- MCUXpresso IDE
- IAR Embedded Workbench for ARM
- Keil uVision MDK
- ArmGCC - GNU Tools ARM Embedded


Running the demo
================
Use the LCD screen to point the camera at handwritten digits. Some images will work better than others, and a few example
digits have been provided in the PDF in the /doc folder. Thicker font works better than thin font and the demo expects 
black ink/marker on a white background.  For best results, the selection rectangle should be centered on the image 
and nearly (but not completely) fill up the whole rectangle. The camera should be stabilized with your finger or by some other means to prevent 
shaking. Also ensure the camera lens has been focused as described in the instructions when connecting the camera and LCD 
(https://community.nxp.com/t5/i-MX-RT-Knowledge-Base/Connecting-camera-and-LCD-to-i-MX-RT-EVKs/tac-p/1122184). 

You will see the result of the inference on the LCD screen as well as the serial terminal. The result printed
on the LCD screen has a minimum threshold applied to it. 
  Top1 class = 4 (4)
  Confidence = 0.999
  Inference time = 10 (ms)

Your own handwritten digits can also be used. It's recommended to use a thick black marker with white paper for best results. 
