Comparison with Alternatives¶
This page compares BlackBox2C with other tools for deploying ML models to embedded systems. All comparisons are based on publicly available documentation and source code.
Feature Matrix¶
| Feature | BlackBox2C | emlearn | MicroMLGen | TFLite Micro | STM32Cube.AI |
|---|---|---|---|---|---|
| Any sklearn model | Yes | Trees only | Trees only | TF/Keras only | TF/Keras only |
| SVM, MLP support | Yes (surrogate) | No | No | Limited | Limited |
| Pure if-else output | Yes | No (arrays) | Yes | No (runtime) | No (runtime) |
| C output | Yes | Yes | Yes | Yes | Yes |
| C++ output | Yes | No | No | No | No |
| Arduino output | Yes | Partial | No | No | No |
| MicroPython output | Yes | No | No | No | No |
| Zero runtime deps | Yes | Yes | Yes | No | No |
| Hardware-agnostic | Yes | Yes | Yes | Partial | No (ST only) |
| Feature selection | Yes | No | No | No | No |
| Memory budget control | Yes | No | No | Partial | Partial |
| Regression support | Yes | Partial | No | Yes | Yes |
| Open source | Yes | Yes | Yes | Yes | No |
emlearn¶
emlearn is a mature library focused on efficient C implementations of decision trees and ensemble models.
emlearn strengths: - Very efficient array-based C for tree ensembles - Supports more tree types natively - More mature and battle-tested
BlackBox2C advantages over emlearn: - Converts any sklearn model (SVM, MLP, etc.) via surrogate extraction - Generates readable if-else code instead of arrays - Multiple output formats (C++, Arduino, MicroPython) - Built-in feature sensitivity analysis
MicroMLGen¶
MicroMLGen generates Arduino-ready C code from sklearn decision trees and SVMs.
MicroMLGen strengths: - Good Arduino/IDE integration - Simple interface for tree models
BlackBox2C advantages over MicroMLGen: - Supports all sklearn models (not just trees/SVM) - Multiple output formats beyond Arduino - Memory budget control - Feature analysis and selection
TensorFlow Lite Micro¶
TFLite Micro is Google's solution for running TensorFlow models on microcontrollers.
TFLite Micro strengths: - Supports complex neural networks - Optimized for ARM Cortex-M with CMSIS-NN - Google-backed, large ecosystem
BlackBox2C advantages over TFLite Micro: - No runtime library required (saves 20-100+ KB) - Works with any C compiler, any MCU - Accepts sklearn models directly - Much simpler integration
When to Use Each¶
| Use case | Recommended tool |
|---|---|
| sklearn model, any MCU, minimal footprint | BlackBox2C |
| Large tree ensemble, performance-critical | emlearn |
| Quick Arduino sketch from decision tree | MicroMLGen |
| Complex neural network, ARM Cortex-M | TFLite Micro |
| ST hardware, Keras model | STM32Cube.AI |