
Nhan Tran, head of Fermilab’s AI Coordination Office, holds a circuit board used for particle tracker data analysis.
Photo Credit: JJ Starr, Fermilab
Scientific Frontline: "At a Glance" Summary
- Main Discovery: Fermilab researchers led the development of hls4ml, an open-source framework capable of embedding neural networks directly into customized digital hardware.
- Methodology: The software automatically translates machine learning code from libraries such as PyTorch and TensorFlow into logic gates compatible with field-programmable gate arrays (FPGAs) and application-specific integrated circuits (ASICs).
- Key Data: Specialized hardware utilizing this framework can execute more than 10 million decisions per second, a necessity for managing the six-fold data increase projected for the High-Luminosity Large Hadron Collider.
- Significance: By processing algorithms in real time with reduced latency and power usage, the system ensures that critical scientific data is identified and stored rather than discarded during high-volume experiments.
- Future Application: Primary deployment targets the CMS experiment trigger system, with broader utility in fusion energy research, neuroscience, and materials science.
- Branch of Science: Particle Physics, Artificial Intelligence, and Microelectronics.
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