hls4ml: From Python Models to Hardware Acceleration

FORMAT: WorkshopLEVEL: Intermediate LANGUAGE: Spanish

Bridge the gap between Python machine learning and hardware implementation using hls4ml. In this workshop, you'll learn how to take ML models trained in Python (TensorFlow, PyTorch, scikit-learn) and deploy them to FPGAs using the hls4ml library. We'll cover model quantization, hardware-aware training, the HLS synthesis workflow, performance profiling, and practical considerations for deploying ML at the edge. No prior FPGA experience required.

Speakers

Jerónimo López Gómez

Researcher @ Universidad de Antioquia

Jerónimo is a researcher at Universidad de Antioquia specializing in hardware-software co-design and machine learning acceleration. He works on deploying ML models to FPGAs and other hardware platforms using hls4ml and Python, bridging the gap between high-level Python ML development and low-level hardware implementation.

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Natalia Echeverri Durán

Researcher @ Universidad de Antioquia

Natalia is a researcher at Universidad de Antioquia focused on machine learning hardware acceleration. She explores techniques for efficiently mapping Python ML models to FPGA implementations for edge computing applications, contributing to research at the frontier of AI hardware co-design.

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