TensorFlores: An enhanced Python-based TinyML framework

• Type: Article in International Scientific Journal
• Date: 2025

Authors

Flores, TKS (Author) Other; Costa, DG (Author) FEUP; Silva, I (Author) Other

Journal

Title: SoftwareX

Vol. 31

Publisher: Elsevier

Indexing

ISI Web of Knowledge - 0 Citations

Scopus - 0 Citations

Other information

Authenticus ID: P-018-YK5

DOI: 10.1016/j.softx.2025.102224

Abstract (EN): 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.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 8