Moath algahtani

Artificial Intelligence Specialization · Cohort 2025

Machine Learning Model Compression for Radiation/Particle Classification on SoC-FPGA

SoC-FPGA technology is increasingly used for hardware acceleration of ML models thanks to its key advantages, including high parallelism, low power consumption, and low latency. However, mapping an ML architecture to a SoC-FPGA often requires the application of compression strategies to create a hardware design that effectively balances model performance, memory footprint, and inference time. This project focuses on employing an end-to-end workflow to map deep neural networks (DNNs) onto SoC-FPGAs for real-time classification of radiation and particles.

Mentor: Romina Molina and Maynor Ballina

Read the final report (PDF)

See it in the program report (PDF)