TensorFlores: An enhanced Python-based TinyML framework
Thommas Kevin Sales Flores, Daniel Gouveia Costa, Ivanovitch Medeiros Dantas da Silva · SoftwareX · 2025
The TensorFlores framework is a Python-based tool designed to optimize machine learning deployment in resource-constrained environments. It introduces evolving clustering-based quantization, supporting both quantization-aware training and post-training quantization while maintaining model accuracy. TensorFlores converts TensorFlow MLP models into optimized formats and generates platform-agnostic C++ code for embedded systems. Its modular architecture minimizes memory usage and computational overhead, enabling efficient real-time inference. By combining clustering-based quantization and automated code generation, TensorFlores enhances TinyML applications, making it a robust solution for low-power and edge AI scenarios in embedded and IoT systems.