At the LHC, data are collected at 40 MHz but only 1 kHz of data can be stored for physics studies. A typical LHC experiment operates a real-time selection system, that has to decide if an event should be stored or discarded. The first stage of this system, the L1 trigger, runs on custom electronic boards, mounting FPGAs. A L1 algorithm needs to operate within O(1μsec) latency. In this system,...
The next generation of particle detectors will feature unprecedented readout rates and require optimizing lossy data compression and transmission from front-end application-specific integrated circuits (ASICs) to the off-detector trigger processing logic. Typically, channel aggregation and thresholding are applied, removing information useful for particle reconstruction in the process. A new...
We present ultra low-latency Deep Neural Networks with large convolutional layers on FPGAs using the hls4ml library. Taking benchmark models trained on public datasets, we discuss various options to reduce the model size and, consequently, the FPGA resource consumption: pruning, quantization to fixed precision, and extreme quantization down to binary or ternary precision. We demonstrate how...
From self-driving cars to particle physics, the uses of convolutional neural networks are plentiful. To greatly decrease inference latency, CNNs and other deep learning architectures can be deployed to hardware compute environments in the form of Field Programmable Gate Arrays (FPGAs). The open source package HLS4ML is leveraged to complete model conversion and RTL synthesis. The work...
Physicists use the Large Hadron Collider (LHC) at CERN/Geneva to create proton-proton (pp) collisions to study rare particle-physics processes at high energies. Within the Phase-II upgrade, the LHC and the particle detectors will be prepared for high luminosity operation, starting in 2027. One challenge is the high level of signal pile-up caused by up to 200 simultaneous pp collisions....
Building on the recent success of deep learning algorithms, Generative Adversarial Networks (GANs) are exploited for modelling the response of the ATLAS detector calorimeter to different particle types and simulating calorimeter showers for photons, electrons and pions over a range of energies (between 256~MeV and 4~TeV) in the full detector η range. The properties of showers in...
A recent effort to explore a neural network inference in FPGAs using High-Level Synthesis language (HLS), focusing on low-latency applications in triggering subsystems of the LHC, resulted in a framework called hls4ml. Deep Learning model converted to HLS using the hls4ml framework can be executed on CPUs, but have subpar performance. We present an extension of hls4ml using the new Intel...
We describe the new Quartus backend of hls4ml, designed to deploy Neural Networks on Intel FPGAs. We list the supported network components and layer architectures (dense, binary/ternary, and convolutional neural networks) and evaluate its performance on a benchmark problem previously considered to develop the Vivado backend of hls4ml. We also introduce the support for recurrent layers and...
Accelerated progress in delivering accurate predictions in science and industry have been accomplished by engaging advanced statistical methods featuring artificial intelligence/deep learning/machine learning (AI/DL/ML). Associated techniques have enabled new avenues of data-driven discovery in key scientific applications areas such as the quest to deliver Fusion Energy – identified by the...
Different groups at CERN have been focusing on changing existing workflows and processes to rely on machine learning, covering trigger farms, fast simulation, anomaly detection, reinforcement learning, etc.
To help end users in these tasks a service must hide the underlying infrastructure complexity and integrate well with existing identity and storage services, as well as easing the tasks...
Experiments at HL-LHC and beyond will have ever higher read-out rate. It is then essential to explore new hardware paradigms for large scale computations. We have considered the Optical Processing Unit (OPU) from LightOn https://lighton.ai , which is an analog device to multiply a binary 1 mega pixel image by a (fixed) 1E6x1E6 random matrix, resulting in a mega pixel image, at a 2kHz rate. It...
In 2026, the LHC will be upgraded to the HL-LHC which will provide up to 10 times as many proton-proton collisions per bunch crossing. In order to keep up with the increase in data rates, the CMS collaboration is updating the Level 1 Trigger system to run particle selection and reconstruction algorithms on FPGAs in real-time with the data collection system. One such particle algorithm measures...
An adversarial mixture density network (AMDN) with gaussian kernels is used to simulate muon reconstruction in the setup of collider detectors. The network is trained on events generated using Madgraph5, Pythia8 and the Delphes3 fast detector simulation implementation for the Compact Muon Solenoid (CMS). It is observed that the network can reproduce relevant kinematic distributions with a very...
Nuclear Track Detectors (NTDs) have been in use for decades,
mainly as detectors of heavily ionizing particles. Existence of natural
thresholds of detection makes them an ideal choice as detectors in the
search for rare, heavily ionizing hypothesized particles (e.g. Monopoles,
Strangelets etc.) against a large low-Z background in cosmic rays as well
as particle accelerators. But...
While the quest for more accurate solutions is pushing deep learning research towards larger and more complex algorithms, edge devices demand efficient inference i.e. reduction in model size, speed and energy consumption. A technique to limit model size is quantization, i.e. using fewer bits to represent weights and biases. Such an approach usually results in a decline in performance. In this...
The Matrix Element Method (MEM) is a powerful method to extract information from measured events at collider experiments. Compared to multivariate techniques built on large sets of experimental data, the MEM does not rely on an examples-based learning phase but directly exploits our knowledge of the physics processes. This comes at a price, both in term of complexity and computing time since...
Machine Learning (ML) is already being used as a powerful tool in High Energy Physics, but the typically high computational cost associated with running ML workloads is often a bottleneck in data processing pipelines. Even on high performance hardware such as Field Programmable Gate Arrays (FPGAs) and Application Specific Integrated Circuits (ASICs) the speed and size of these models are often...
We describe a method for precisely regulating the gradient magnet power supply (GMPS) at the Fermilab Booster accelerator complex using a neural network (NN). We demonstrate preliminary results by training a surrogate machine-learning model on real accelerator data, and using the surrogate model in turn to train the NN for its regulation task. We additionally show how the neural networks that...
FPGA programming is becoming easier as the vendors begin to provide environments, such as for machine learning (ML), that enable programming at higher levels of abstraction.The vendor platforms target FPGAs in a single host server.To scale to larger systems of FPGAs requires communication through the hosts, which has a significant impact on performance. We demonstrate the deployment of ML...
The CONNIE experiment (Coherent Neutrino-Nucleus Interaction Experiment) is a collaboration from some countries in South America, EEUU and Switzerland . The data collected during the CONNIE experiment can be used to search for time variations of particles arriving at the detectors with periodic and stochastic nature. This experiment uses 12 high resistivity CCDs (Charge-Coupled Devices) placed...
Transition Radiation Detectors (TRD) have the attractive features of being able to separate particles by their gamma factor. A new TRD development, based on a GEM technology, is being carried out as a R&D project for the future Electron Ion Collider (EIC) and for upgrade of the GlueX experiment. This detector combines a high precision GEM tracker with TRD functionality and optimized for...
In this work we describe the development of machine learning models to assist the CLAS12 detector tracking algorithm. Several networks were implemented to assist tracking algorithm to overcome drift chambers inefficiencies using auto-encoders to de-noise wire chamber signals and corruption detection.A classifier network was used to identify track candidates from numerous combinatorial segments...
We describe anomaly detection applications on Neuromorphic Chips, exploiting Spiking Neural Networks on the Intel Loihi chip. We describe different workflows to train models directly on Loihi or to convert Neural Networks to Spiking Neural Networks. As a benchmark, we consider the problem of Gravitational Wave detection without a-priori assumption of the wave profile. We discuss baseline...
Current charged particle tracking algorithms at the CERN Large Hadron Collider (LHC) scale quadratically or worse with increasing number of overlapping proton-proton collisions in an event (pileup). As the LHC moves into its high-luminosity phase, pileup is expected to increase to an average of 200 overlapping collisions, highlighting the need for new algorithmic strategies. Recent work has...
In the next decade, the demands for computing in large scientific experiments are expected to grow tremendously. During the same time period, CPU performance increases will be limited. At the CERN Large Hadron Collider (LHC), these two issues will confront one another as the collider is upgraded for high luminosity running. Alternative processors such as graphics processing units (GPUs) can...
