TensorFlow to MPLAB® Harmony v3 Model Converter

Welcome to tf2mplabh3!

This project is proudly developed and maintained by Microchip Technology Inc.
It enables you to convert TensorFlow models to C code, ready for seamless integration into your MPLAB® Harmony v3 embedded projects.

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What is this?

tf2mplabh3 is a command-line tool that automates the conversion of TensorFlow models to C code, making it easy to deploy machine learning models on embedded systems using MPLAB® Harmony v3.

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Table of Contents

• Features
• Installation
• Quick Start
• Usage
• Arguments
• Examples
• How to use the hardware capabilities to accelerate inference
• Benchmarking
• License
• Acknowledgments

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Features

• Convert TensorFlow SavedModel to C code
• Easy CLI interface
• Verbosity control for logging
• Ready for integration with MPLAB Harmony v3

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Installation

Clone the repository and run the installation script:

git clone --recursive https://github.com/MicrochipTech/tf2mplabh3.git
cd tf2mplabh3
sudo ./install.sh

If you already cloned without --recursive, run:

git submodule update --init --recursive

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Quick Start

Activate the virtual environment and run the script, by passing the TensorFlow model path as shown in the example below:

source .venv/bin/activate
python3 -m tf2mplabh3 -m examples/mobilenet-v2-tensorflow2-035-128-classification-v2

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Usage

python3 -m tf2mplabh3 [options]

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Arguments

Argument	Description	Default
-m, --model	Path to TensorFlow SavedModel directory	examples/mobilenet-v2-tensorflow2-035-128-classification-v2
-onnx, --onnx_model	Path to output ONNX intermediate model file	examples/model.onnx
-c_file, --c_model_file	Path to output C model file	examples/model.c
--tag	SavedModel tag (e.g., serve)	None
--signature_def	Signature def key (e.g., serving_default)	None
--onnx2c	Path to the onnx2c executable	c_deps/onnx2c/build/onnx2c
-v, --verbosity	Verbosity level (0=quiet, 1=all logs)	0
--overwrite	Overwrite existing ONNX or C model files	Not used

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Examples

Convert a model with default settings:

python3 -m tf2mplabh3

Convert a specific model and increase verbosity:

python3 -m tf2mplabh3 -m path/to/model -v 1

How to use the hardware capabilities to accelerate inference:

In order to ensure an optimized inference time, leverage the features of the MPLAB® XC-32 Compilers by activating the third level of compilation in your MPLAB® X project. Doing this ensures a extended use of the hardware capabilities of the device.

As shown in the example image below:

Benchmarking

The following table shows the inference time for the example model (mobilenet-v2-tensorflow2-035-128-classification-v2) converted and run with different optimization levels.

All benchmarks were performed on:

• Hardware: Microchip SAMA5D29 Curiosity Development Board
• CPU: 1x ARM® Cortex®-A5
• Clock Frequency: 498 MHz
• Compiler: XC32 v4.30

Optimization Level	Inference Time (ms)	Notes/Flags Used
None	7536.600	No optimization
-O1	1730.550	Basic optimization
-O2	1368.060	More optimization
-O3	1188.790	Optimize for speed
-Os	1382.300	Optimize for size

Note:
Inference time was measured as the average over 100 runs.
Results may vary depending on compiler version, memory configuration, and other system activity.

License

Apache-2.0 License

Acknowledgments

• tf2onnx
• onnx2c
• TensorFlow