SOFTWARE: MCUXpresso Integrated Development Environment (IDE)
The MCUXpresso IDE brings developers an easy-to-use Eclipse-based development environment for NXP MCUs based on Arm Cortex-M cores, including its general purpose crossover and wireless - enabled MCUs. The MCUXpresso IDE offers advanced editing, compiling, and debugging features with the addition of MCU-specific debugging views, code trace and profiling, multicore debugging, and integrated configuration tools. The MCUXpresso IDE debug connections support all general purpose Arm Cortex-M based EVKs and your custom development boards with optimized open-source and commercial debug probes from NXP, P&E Micro, and SEGGER.
MCUXpresso Integrated Development Environment (IDE) download
https://www.nxp.com/webapp/swlicensing/sso/downloadSoftware.sp?catid=MCUXPRESSO
eIQ ML Software Development Environment
The NXP eIQ machine learning (ML) software development environment enables the use of ML algorithms on NXP EdgeVerse microcontrollers and microprocessors, including i.MX RT crossover MCUs, and i.MX family application processors. eIQ ML software includes a ML workflow tool called eIQ Toolkit, along with inference engines, neural network compilers and optimized libraries. This software leverages open-source and proprietary technologies and is fully integrated into our MCUXpresso SDK and Yocto development environments, allowing you to develop complete system-level applications with ease.
The CIFAR-10 dataset
The CIFAR-10 dataset consists of 60000 32x32 color images in 10 classes, with 6000 images per class. There are 50000 training images and 10000 test images. The dataset is divided into five training batches and one test batch, each with 10000 images. The test batch contains exactly 1000 randomly-selected images from each class. The training batches contain the remaining images in random order, but some training batches may contain more images from one class than another. Between them, the training batches contain exactly 5000 images from each class.
Here are the classes in the dataset, as well as 10 random images from each:
The CIFAR-100 dataset
This dataset is just like the CIFAR-10, except it has 100 classes containing 600 images each. There are 500 training images and 100 testing images per class. The 100 classes in the CIFAR-100 are grouped into 20 superclasses. Each image comes with a "fine" label (the class to which it belongs) and a "coarse" label (the superclass to which it belongs).
Here is the list of classes in the CIFAR-100:
Superclass: Classes
aquatic mammals: beaver, dolphin, otter, seal, whale
fish: aquarium fish, flatfish, ray, shark, trout
flowers: orchids, poppies, roses, sunflowers, tulips
food containers: bottles, bowls, cans, cups, plates
fruit and vegetables: apples, mushrooms, oranges, pears, sweet peppers
household electrical devices: clock, computer keyboard, lamp, telephone, television
household furniture: bed, chair, couch, table, wardrobe
insects: bee, beetle, butterfly, caterpillar, cockroach
large carnivores: bear, leopard, lion, tiger, wolf
large man-made outdoor things: bridge, castle, house, road, skyscraper
large natural outdoor scenes: cloud, forest, mountain, plain, sea
large omnivores and herbivores: camel, cattle, chimpanzee, elephant, kangaroo
medium-sized mammals: fox, porcupine, possum, raccoon, skunk
non-insect invertebrates: crab, lobster, snail, spider, worm
people: baby, boy, girl, man, woman
reptiles: crocodile, dinosaur, lizard, snake, turtle
small mammals: hamster, mouse, rabbit, shrew, squirrel
trees: maple, oak, palm, pine, willow
vehicles 1: bicycle, bus, motorcycle, pickup truck, train
vehicles 2: lawn-mower, rocket, streetcar, tank, tractor
Example application with MCXN947 for ML:
Train and Deploy Customer ML model to NPU
Part 1: Introduction
The eIQ Neutron Neural Processing Unit (NPU) is a highly scalable accelerator core architecture that provides machine learning (ML) acceleration. The eIQ Neutron NPU offers up to 42 times faster machine learning inference performance compared to a standalone CPU core. Specifically, the MCX N94 can execute 4.8G (150MHz * 4 * 4 * 2) INT8 operations per second. The eIQ Portal, developed in exclusive partnership with Au-Zone Technologies, is an intuitive graphical user interface (GUI) that simplifies vision based ML solutions development. Developers can create, optimize, debug and export ML models, as well as import datasets and models, rapidly train and deploy neural network models and ML workloads for vision applications.
Hardware Environment:
Development Board: FRDM-MCXN947
Display: 3.5" TFT LCD (Part Number: PAR-LCD-S035)
Camera: OV7670
Software Environment:
eIQ Portal: eIQ® ML Software Development Environment | NXP Semiconductors
MCUXpresso IDE v11.9.0
Application Code Hub Demo: Label CIFAR10 image
Part 2: Basic Model Classification Training and Deployment
The main content is divided into three steps: model training, model converting, and model deployment. following step to follow
1. Dataset Preparation
2. Create Project and Import Dataset into eIQ
3. Select Base Model for Training
4. Model Evaluation "VALIDATE"
5. Model Export to TensorFlow Lite
6. Convert to TensorFlow Lite for Neutron (.tflite)
7. Deploy the Model to the Label CIFAR10 Image Project
Part 3: Experimental Results
Part 4: Summary
By efficiently utilizing the powerful performance of the eIQ Neutron NPU and the convenient tools of the eIQ Portal, developers can significantly streamline the entire process from model training to deployment. This not only accelerates the development cycle of machine learning applications but also enhances their performance and reliability. Therefore, for developers looking to implement efficient machine learning applications on MCX N-series edge devices, mastering these technologies and tools is crucial.
for more details follow the URL https://community.nxp.com/t5/MCX-Microcontrollers-Knowledge/MCXN947-How-to-Train-and-Deploy-Customer-ML-model-to-NPU/ta-p/1899497
