hls4ml: From Python Models to Hardware Acceleration

FORMAT: WorkshopLEVEL: Intermediate LANGUAGE: Spanish

This session presents a journey from developing machine learning models in Python to their implementation in hardware through the hls4ml tool. The goal is to show how models built in widely used frameworks such as TensorFlow, Keras, or PyTorch can be transformed into efficient hardware descriptions for deployment on reconfigurable devices. The complete workflow will be covered, including model preparation, conversion to High Level Synthesis (HLS) code, and the main hardware optimization criteria such as quantization and precision reduction. The trade-offs between latency, energy consumption, and model accuracy will also be discussed.

Speakers

Jeronimo Lopez Gomez

Estudiante, Joven Investigador @ Universidad de Antioquia, Grupo de Instrumentacion Cientifica y Microelectronica

Currently completing my undergraduate degree in Physics at Universidad de Antioquia, I have experience in signal processing, high-performance data acquisition systems, and development of solutions for scientific instrumentation. I have worked on the design and implementation of solutions based on reconfigurable hardware, Xilinx System on Chip (SoC) architectures, with data analysis capabilities in Python, modeling, integrations with embedded systems, and computer vision. Passionate about technology, I consider physics a transversal training that strengthens analysis, modeling, and the ability to address complex problems in different technological contexts.

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Natalia Echeverri Durán

Estudiante @ Universidad de Antioquia, Grupo de Instrumentacion Cientifica y Microelectronica

Hello! I am Natalia, a final-year Physics student at Universidad de Antioquia (Colombia), and I am currently taking an AI bootcamp. I am passionate about applying machine learning and data science to real-world problems, from physical systems to behavioral data. I believe physics teaches you to see the world through patterns and equations, and AI gives you the tools to act on them.

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