CERN Embeds AI Directly Into Silicon to Tackle Data Deluge from Large Hadron Collider

The European Organization for Nuclear Research (CERN) is taking an unconventional approach to managing the massive data streams generated by the Large Hadron Collider (LHC). Rather than relying on traditional computing infrastructure, CERN is “burning” custom artificial intelligence directly into the silicon of its detectors to filter data in real-time, a strategy born out of necessity to handle the LHC’s overwhelming output.

The Scale of the Challenge

The LHC produces approximately 40,000 exabytes (EBs) of unfiltered sensor data annually, equivalent to roughly a quarter of the entire internet’s data volume [1]. This immense quantity of data far exceeds CERN’s storage capacity, necessitating aggressive real-time reduction techniques. The LHC detector systems process data at speeds up to hundreds of terabytes per second, significantly faster than the data processing rates of companies like Google or Netflix.

Anomaly Detection at the Edge

Thea Aarrestad, an assistant professor of particle physics at ETH Zurich and researcher at CERN, presented this innovative approach at the virtual Monster Scale Summit earlier this month [2]. Aarrestad’s work focuses on anomaly detection, a critical component of data observability. The core of the solution involves embedding anomaly-detection algorithms directly into Application-Specific Integrated Circuits (ASICs) and Field-Programmable Gate Arrays (FPGAs) within the detectors.

How It Works: The Level One Trigger

Detectors buffer captured data for up to 4 microseconds. If the data isn’t saved within that timeframe, it’s lost. The decision to preserve or discard data is made by the “Level One Trigger,” a system comprised of approximately 1,000 FPGAs. This trigger receives reduced event information via fiber optic lines at a rate of 10 terabytes per second and outputs a binary decision: accept (1) or reject (0) [1].

AI Optimized for Speed and Efficiency

Traditional AI models are often too large and computationally intensive for this application. CERN engineers have developed techniques to create highly optimized models. These models are “trained to be small from the get-travel,” employing quantization, pruning, parallelization, and distillation to reduce their size and complexity [1]. The team similarly developed a transpiler, HLS4ML, to convert models into C++ code optimized for specific hardware platforms, including ASICs and FPGAs.

Tree-Based Models and the Standard Model

CERN found that tree-based models offer comparable performance to deep learning models but with significantly lower computational costs. This is particularly well-suited to the nature of the data generated by the LHC, which can be viewed as a collection of tabular data representing discrete measurements from particle collisions [1].

Future Upgrades and the High Luminosity LHC

The LHC is currently undergoing upgrades to develop into the High Luminosity LHC, scheduled to become operational in 2031. This upgrade will increase the collision rate and data volume by a factor of ten, pushing data rates to 63 terabytes per second [1]. The upgraded detectors will be capable of identifying each collision and tracking particle pairings with greater precision.

A Shift in AI Development

While many AI labs are focused on building ever-larger models, CERN is pursuing a different path – creating smaller, faster, and more efficient AI algorithms tailored to the unique demands of high-energy physics. This approach highlights the importance of adapting AI techniques to specific applications and recognizing that, in some cases, knowing what data to discard is as crucial as knowing what data to keep.