TinyML at CERN: How FPGAs and hls4ml Solve Physics' Biggest Data Problem
Forty million times per second, proton bunches collide inside the Large Hadron Collider. Each crossing can produce dozens of simultaneous collisions, generating up to a billion particle interactions per second at high luminosity. The raw data rate from CERN's main detectors hits multiple petabytes per second. That's more data than every internet backbone on Earth combined. And the part that still blows my mind: the system that decides what to keep and what to throw away is running TinyML at CERN on silicon that fits in your hand.
This isn't a chatbot. It's not generating images. It's a neural network burned into an FPGA, making life-or-death decisions for particle physics at speeds that would make your GPU weep. The tool that makes it all work is called hls4ml, and it's completely open source.
The Data Problem That Makes "Big Data" Look Quaint
Let's put CERN's data challenge in perspective. The LHC's bunch crossing rate is 40 MHz. That's 40 million potential collision events every single second. With multiple collisions per crossing, especially as the High-Luminosity LHC upgrade ramps up, total particle interactions reach into the hundreds of millions to over a billion per second.
Only about 0.01% of these events contain physics worth studying. The rest is noise. But you can't just store everything and sort it out later. At multiple petabytes per second of raw sensor data, there isn't enough storage on the planet to hold even a few minutes of unfiltered output.
So CERN uses a "trigger system." It's a multi-stage filter that decides in real time which collision events to save and which to discard forever. The Level-1 (L1) trigger is the first and most brutal stage. It operates within a latency budget of roughly 3-4 microseconds (about 3.8 microseconds for CMS, around 2.5 for ATLAS). Within that window, the system must read raw detector signals, reconstruct basic particle trajectories, and make a keep-or-discard decision.
The margin for error is essentially zero. If you throw away an event containing evidence of new physics, it's gone. No undo button. No replay.
Having worked on systems where AI latency directly determines product viability, I find CERN's constraint almost absurdly extreme. In web services, a few hundred milliseconds of latency degrades user experience. At the LHC, a few microseconds of latency means losing data that took billions of dollars to produce.
Why GPUs Can't Touch This Problem
When most engineers think "fast AI inference," they think GPUs. Makes sense. GPUs dominate machine learning for good reason. But the L1 trigger system can't use them. Not because they're not fast enough in raw throughput, but because their latency profile is completely wrong.
A GPU processes data in batches. Data moves from memory to the GPU, gets processed, results move back. Even on the fastest NVIDIA hardware, this round-trip takes milliseconds. That's a thousand times too slow for the L1 trigger.
What CERN needs is inference in the nanosecond regime. Not millisecond. Not microsecond. Nanosecond. The ML inference portion of the trigger pipeline needs to complete in roughly 100 nanoseconds to leave enough headroom for the rest of the trigger logic within that 3-4 microsecond budget.
FPGAs solve this because they work fundamentally differently. Unlike a GPU, an FPGA doesn't execute instructions sequentially. You configure its logic gates to physically implement the computation in hardware. Data flows through the chip like water through pipes. No instruction fetch, no memory bus bottleneck, no batch scheduling. The signal goes in one side and the answer comes out the other side a few clock cycles later.
I've benchmarked local LLM inference against cloud APIs, and even the fastest local GPU setup operates in a completely different universe from what FPGAs achieve. We're talking four to five orders of magnitude difference in latency.
What Is hls4ml and How Does It Work?
Here's the hard part: programming FPGAs is notoriously difficult. You typically write in hardware description languages like VHDL or Verilog. It's closer to circuit design than software engineering. Most ML researchers don't know HDL. Most HDL engineers don't know ML. This was the bottleneck that kept FPGA-based inference locked behind a tiny group of specialists.
hls4ml — High-Level Synthesis for Machine Learning — bridges that gap. Developed by a collaboration spanning Fermilab, CERN, MIT, and several other institutions, it's an open-source tool that translates standard ML models from frameworks like TensorFlow and PyTorch directly into FPGA firmware.
The workflow is straightforward. You train a neural network using your normal ML tools. You feed the trained model into hls4ml. It generates synthesizable HLS code that can be compiled onto an FPGA. The output is a neural network running entirely in hardware, with inference latencies as low as 100 nanoseconds.
The real win isn't just speed. It's that hls4ml makes extreme-performance inference accessible to scientists who aren't hardware engineers. That matters. A lot. What was previously an incredibly specialized skill is now something a physics grad student can do with a Python script.
The project is detailed in a 2021 paper on arXiv (2103.05579) by Farah Fahim, Nhan Tran, and nearly 30 collaborators. It's grown into a thriving open-source community with about 1,900 stars on GitHub. As Dylan Rankin, a physicist at MIT and one of the contributors, has emphasized, the tool provides an open-source alternative to proprietary commercial solutions for fast ML inference on hardware.
The Art of Making Neural Networks Tiny Enough for Silicon
You can't take a 100-million-parameter transformer and shove it onto an FPGA. The models running in the L1 trigger are aggressively compressed. We're talking networks with hundreds or low thousands of parameters. Not millions. Not billions.
This is where TinyML techniques become critical. The hls4ml workflow incorporates several compression strategies:
- Quantization: Reducing weight precision from 32-bit floating point down to 2-6 bits. Every bit you save translates directly to fewer logic gates on the FPGA.
- Pruning: Removing weights that don't significantly affect the output. Some networks end up with 90%+ of weights zeroed out.
- Quantization-aware training: Training the network knowing it will be quantized, so it learns to maintain accuracy at reduced precision.
- Architecture search: Designing network architectures specifically for FPGA resource constraints rather than retrofitting GPU-oriented models.
A neural network that classifies particle jets might use fewer parameters than a single attention head in GPT-4, yet it achieves classification accuracy that matches or exceeds traditional physics algorithms.
This is one of those things where the boring answer is actually the right one. The flashy trend in AI is making models bigger. The hard, unglamorous work at CERN is making them as small as physically possible while maintaining accuracy. That constraint is producing some of the most elegant engineering I've seen in my career.
Why This Matters Beyond Particle Physics
If you're thinking "cool, but I don't work at CERN," stay with me. The techniques pioneered by hls4ml are spreading.
Nhan Tran, a scientist at Fermilab and a driving force behind the project, has pointed out that the same approach applies anywhere you need ultra-fast, ultra-low-power inference at the edge. Autonomous vehicles that need to process LiDAR in microseconds. Medical devices doing real-time signal processing. Satellite systems that can't afford to beam raw data back to Earth.
The edge AI movement is one of the most exciting trends in computing right now, but most of the discourse focuses on running models on phones or IoT devices. CERN's work is the extreme end of that spectrum: AI so fast and so efficient it runs at the speed of physics itself.
And there's a lesson here about constraint-driven engineering that I keep coming back to. When you have infinite compute (or close enough), you build bloated models and call it progress. When your latency budget is measured in nanoseconds and your power budget is measured in watts, you're forced to actually think about what a neural network needs to be.
I've shipped enough production systems to know that constraints breed better engineering. The tech debt that accumulates in unconstrained AI systems is real and growing. CERN's approach is the opposite philosophy: every parameter earns its place, every bit of precision is justified, every microsecond is accounted for.
The Future: When Every Sensor Gets a Brain
The High-Luminosity LHC upgrade, expected to be fully operational in the coming years, will increase the collision rate by a factor of five to seven beyond current levels. The trigger system will need to get dramatically smarter. The hls4ml team is already working on next-generation models that handle this increased complexity while staying within the same brutal latency constraints.
But the bigger story is what happens when these tools mature and spread. We're heading toward a world where ML inference doesn't happen in data centers. It happens in the sensor. In the chip. In the physical substrate of the detector itself. CERN is the first and most extreme proving ground.
If you're building systems where latency matters — and in 2026, that's increasingly all of us — pay attention to what the particle physicists are doing. They solved the "fast inference" problem years ago. They just did it with FPGAs and open-source tools instead of billion-dollar GPU clusters.
The next breakthrough in AI might not come from making models bigger. It might come from making them small enough to fit on a chip that decides the fate of a particle in 100 nanoseconds.
Originally published on kunalganglani.com
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