AN 1011: TinyML Applications in Altera FPGAs Using LiteRT for Microcontrollers

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ID 848984
Date 9/29/2025
Public
Document Table of Contents
Document Table of Contents x
1. Overview 2. Preparing LiteRT Inference Model 3. Generating Nios® V Processor System 4. Generating Arm Processor System 5. Programming and Running 6. Nios® V Processor with TinyML Design Example 7. Appendix 8. Document Revision History for the AN 1011: TinyML Applications in Altera FPGAs Using LiteRT for Microcontrollers
1. Overview x
1.1. Requirements
2. Preparing LiteRT Inference Model x
2.1. Defining the Problem 2.2. Gathering and Preparing Sample Data 2.3. Building TensorFlow Model 2.4. Preparing the LiteRT Model 2.5. Preparing Binaries for C/C++
2.2. Gathering and Preparing Sample Data x
2.2.1. Preparing Dataset 2.2.2. Preprocessing Dataset
2.3. Building TensorFlow Model x
2.3.1. Constructing Model Architecture 2.3.2. Configuring Model 2.3.3. Training Model 2.3.4. Evaluating the Model 2.3.5. Saving and Loading Model
2.4. Preparing the LiteRT Model x
2.4.1. Converting into LiteRT Model 2.4.2. Saving the LiteRT Model 2.4.3. Analyzing the LiteRT Model 2.4.4. Loading a LiteRT Interpreter 2.4.5. Evaluating the LiteRT Model
2.5. Preparing Binaries for C/C++ x
2.5.1. Converting LiteRT to a C Array 2.5.2. Preparing a Header File for LiteRT C Array 2.5.3. Preparing Supporting Header File for LiteRT C Array Content of model_settings.h 2.5.4. Preparing Main Function to Run TinyML 2.5.5. Converting MNIST Sample into C Array
3. Generating Nios® V Processor System x
3.1. Building Hardware Design in Platform Designer 3.2. Building Software Design with Ashling* RiscFree* IDE for Altera® FPGAs
3.2. Building Software Design with Ashling* RiscFree* IDE for Altera® FPGAs x
3.2.1. Creating a Board Support Package 3.2.2. Building LiteRT for Microcontroller Static Library 3.2.3. Creating an Application Project File 3.2.4. Building the LiteRT TinyML Application
4. Generating Arm Processor System x
4.1. GHRD Hardware Design 4.2. Building the Software Design
4.2. Building the Software Design x
4.2.1. Arm-Trusted-Firmware 4.2.2. Building LiteRT for Microcontrollers Static Library 4.2.3. Zephyr OS Environment Setup 4.2.4. Building the LiteRT TinyML Application
4.2.1. Arm-Trusted-Firmware x
4.2.1.1. Booting from QSPI Flash 4.2.1.2. Running through Ashling* RiscFree* IDE for Altera® FPGAs Debugger
5. Programming and Running x
5.1. Nios® V Processor System 5.2. Arm HPS Processor System
5.2. Arm HPS Processor System x
5.2.1. Booting from QSPI 5.2.2. Booting via the Debugger
6. Nios® V Processor with TinyML Design Example x
6.1. Finding Nios® V Processor with TinyML Design Example
7. Appendix x
7.1. main.cc for Nios V Processor 7.2. main.cc for Arm HPS Processor
1. Overview
2. Preparing LiteRT Inference Model
3. Generating Nios® V Processor System
4. Generating Arm Processor System
5. Programming and Running
6. Nios® V Processor with TinyML Design Example
7. Appendix
8. Document Revision History for the AN 1011: TinyML Applications in Altera FPGAs Using LiteRT for Microcontrollers

2.5.3. Preparing Supporting Header File for LiteRT C Array

The example main() function implements the supporting model_settings.h header file to decode the output of the LiteRT model.

Content of model_settings.h

#ifndef IMAGE_CLASSIFICATION_MODEL_SETTINGS_H_
#define IMAGE_CLASSIFICATION_MODEL_SETTINGS_H_

// Keeping these as constant expressions allow us to allocate fixed-sized arrays
// on the stack for our working memory.

// All of these values are derived from the values used during model training,
// if you change your model you'll need to update these constants.
constexpr int kNumCols = 28;
constexpr int kNumRows = 28;
constexpr int kNumChannels = 1;

constexpr int kMaxImageSize = kNumCols * kNumRows * kNumChannels;

constexpr int kCategoryCount = 10;
constexpr int k0Index	= 0;
constexpr int k1Index	= 1;
constexpr int k2Index	= 2;
constexpr int k3Index	= 3;
constexpr int k4Index	= 4;
constexpr int k5Index	= 5;
constexpr int k6Index	= 6;
constexpr int k7Index	= 7;
constexpr int k8Index	= 8;
constexpr int k9Index	= 9;

constexpr const char* kCategoryLabels[kCategoryCount] = {
  "number 0",
  "number 1",
  "number 2",
  "number 3",
  "number 4",
  "number 5",
  "number 6",
  "number 7",
  "number 8",
  "number 9"
};

#endif  // IMAGE_CLASSIFICATION_MODEL_SETTINGS_H_
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