Friday 28 February 2025
The quest for faster and more efficient data processing is a constant one in the world of high-energy physics. Researchers at CERN’s Compact Muon Solenoid (CMS) experiment have made significant strides in this area, developing an innovative approach to accelerate the processing of massive amounts of data.
At the heart of the CMS experiment lies the Level-1 trigger system, responsible for filtering out irrelevant data and identifying potential collisions of interest. The current system relies on custom-built hardware, which can become outdated as new technologies emerge. To address this issue, scientists have turned to high-level synthesis (HLS), a technique that allows them to translate complex algorithms into efficient hardware designs.
The team’s solution involves using HLS to implement the Overlap Muon Track Finder (OMTF) algorithm on field-programmable gate arrays (FPGAs). This approach enables them to create custom-designed hardware accelerators, tailored specifically for the OMTF algorithm. By leveraging the capabilities of FPGAs, researchers can reduce processing times and increase the system’s overall efficiency.
One of the key challenges faced by the team was integrating the HLS design with the existing Level-1 trigger system. To overcome this hurdle, they developed a custom-built framework that automates the process of hardware integration and bitstream generation. This innovative approach enables seamless communication between the FPGA-based accelerators and the rest of the system.
The results are impressive: the new implementation achieves a significant reduction in latency, processing times, and resource utilization compared to the previous system. The improved performance is attributed to the efficient use of parallel processing, pipelining, and memory optimization techniques. These advancements have far-reaching implications for high-energy physics research, enabling scientists to analyze larger datasets and identify previously undetectable phenomena.
The success of this project demonstrates the potential of HLS in accelerating data processing for high-energy physics experiments. As researchers continue to push the boundaries of what is possible, this technique will likely play a crucial role in shaping the future of data analysis in these fields.
Cite this article: “Accelerating Data Processing at CERNs CMS Experiment with High-Level Synthesis”, The Science Archive, 2025.
High-Energy Physics, Data Processing, Level-1 Trigger System, Compact Muon Solenoid, Hls, Fpgas, Hardware Acceleration, Overlap Muon Track Finder, Latency Reduction, Parallel Processing.
