Recently, compelling evidence for the emission of high-energy neutrinos from our host Galaxy - the Milky Way - was reported by IceCube, a neutrino detector instrumenting a cubic kilometer of glacial ice at the South Pole. This breakthrough observation is enabled by advances in AI, including a physics-driven deep learning method capable of exploiting available symmetries and domain knowledge....
This R&D project, initiated by the DOE Nuclear Physics AI-Machine Learning initiative in 2022, explores advanced AI technologies to address data processing challenges at RHIC and future EIC experiments. The main objective is to develop a demonstrator capable of efficient online identification of heavy-flavor events in proton-proton collisions (~1 MHz) based on their decay topologies, while...
Attention-based transformers are ubiquitous in machine learning applications from natural language processing to computer vision. In high energy physics, one central application is to classify collimated particle showers in colliders based on the particle of origin, known as jet tagging. In this work, we study the interpretatbility and prospects for acceleration of Particle Transformer (ParT),...
In this work, we present the Scalable QUantization-Aware Real-time Keras (S-QUARK), an advanced quantization-aware training (QAT) framework for efficient FPGAs inference built on top of Keras-v3, supporting all Tensorflow, JAX, and PyTorch backends.
The framework inherits all perks from the High Granularity Quantization (HGQ) library, and extends it to support fixed-point numbers with...
The next phase of high energy particle physics research at CERN will
involve the High-Luminosity Large Hadron Collider (HL-LHC). In preparation for
this phase, the ATLAS Trigger and Data AcQuisition (TDAQ) system will undergo
upgrades to the online software tracking capabilities. Studies are underway to
assess a heterogeneous computing farm deploying GPUs and/or FPGAs, together
with the...
An Artificial Intelligence (AI) model will spend โ90% of its lifetime in inference.โTo fully utilize co-
processors, such as FPGAs or GPUs, for AI inference requires O(10) CPU cores to feed to work to the
coprocessors. Traditional data analysis pipelines will not be able to effectively and efficiently use
the coprocessors to their full potential. To allow for distributed access to...
Processing large volumes of sparse neutrino interaction data is essential to the success of liquid argon time projection chamber (LArTPC) experiments such as DUNE. High rates of radiological background must be eliminated to extract critical information for track reconstruction and downstream analysis. Given the computational load of this rejection, and potential real time constraints of...
Detector simulation is a key component of physics analysis and related activities in particle physics.In the upcoming High Luminosity LHC era, simulation will be required to use a smaller fraction of computing in order to satisfy resource constraints at the same time as experiments are being upgraded new with the new higher granularity detectors, which requires significantly more resources to...
The demand for machine learning algorithms on edge devices, such as Field-Programmable Gate Arrays (FPGAs), arises from the need to process and intelligently reduce vast amounts of data in real-time, especially in large-scale experiments like the Deep Underground Neutrino Experiment (DUNE). Traditional methods, such as thresholding, clustering, multiplicity checks, or coincidence checks,...
Detecting quenches in superconducting (SC) magnets by non-invasive means is a challenging real-time process that involves capturing
and sorting through physical events that occur at different frequencies and appear as various signal features. These events may be correlated across instrumentation type, thermal cycle, and ramp. These events together build a more complete picture of continuous...
Reinforcement Learning (RL) is a promising approach for the autonomous AI-based control of particle accelerators. Real-time requirements for these algorithms can often not be satisfied with conventional hardware platforms.
In this contribution, the unique KINGFISHER platform being developed at KIT will be presented. Based on the novel AMD-Xilinx Versal platform, this system provides...
AI Red Teaming, an offshoot of traditional cybersecurity practices, has emerged as a critical tool for ensuring the integrity of AI systems. An under explored area has been the application of AI Red Teaming methodologies to scientific applications, which increasingly use machine learning models in workflows. I'll highlight why this is important and how AI Red Teaming can highlight...
Neural networks with a latency requirement at the order of microseconds, like the ones used at the CERN Large Hadron Colliders, are typically deployed on FPGAs fully unrolled. A bottleneck for the deployment of such neural networks is area utilization, which is directly related to the number of Multiply Accumulate (MAC) operations in matrix-vector multiplications.
In this work, we present...
In the search for new physics, real-time detection of anomalous events is critical for maximizing the discovery potential of the LHC. CICADA (Calorimeter Image Convolutional Anomaly Detection Algorithm) is a novel CMS trigger algorithm operating at the 40 MHz collision rate. By leveraging unsupervised deep learning techniques, CICADA aims to enable physics-model independent trigger decisions,...
Unsupervised learning algorithms enable insights from large, unlabeled datasets, allowing for feature extraction and anomaly detection that can reveal latent patterns and relationships often not found by supervised or classical algorithms. Modern particle detectors, including liquid argon time projection chambers (LArTPCs), collect a vast amount of data, making it impractical to save...
Low latency machine learning inference is vital for many high-speed imaging applications across various scientific domains. From analyzing fusion plasma [1] to rapid cell-sorting [2], there is a need for in-situ fast inference in experiments operating in the kHz to MHz range. External PCIe accelerators are often unsuitable for these experiments due to the associated data transfer overhead,...
Recent advancements in generative artificial intelligence (AI), including transformers, adversarial networks, and diffusion models, have demonstrated significant potential across various fields, from creative art to drug discovery. Leveraging these models in engineering applications, particularly in nanophotonics, is an emerging frontier. Nanophotonic metasurfaces, which manipulate light at...
Applications like high-energy physics and cybersecurity require extremely high throughput and low latency neural network (NN) inference. Lookup-table-based NNs address these constraints by implementing NNs purely as lookup tables (LUTs), achieving inference latency on the order of nanoseconds. Since LUTs are a fundamental FPGA building block, LUT-based NNs map to FPGAs easily. LogicNets (and...
Recent advancements in Vision-Language Models (VLMs) have enabled complex multimodal tasks by processing text and image data simultaneously, significantly enhancing the field of artificial intelligence. However, these models often exhibit biases that can skew outputs towards societal stereotypes, thus necessitating debiasing strategies. Existing debiasing methods focus narrowly on specific...
As machine learning (ML) increasingly serves as a tool for addressing real-time challenges in scientific applications, the development of advanced tooling has significantly reduced the time required to iterate on various designs. Despite these advancements in areas that once posed major obstacles, newer challenges have emerged. For example, processes that were not previously considered...
We develop an automated pipeline to streamline neural architecture codesign for physics applications, to reduce the need for ML expertise when designing models for a novel task. Our method employs a two-stage neural architecture search (NAS) design to enhance these models, including hardware costs, leading to the discovery of more hardware-efficient neural architectures. The global search...
Deep learning, particularly employing the Unet architecture, has become pivotal in cardiology, facilitating detailed analysis of heart anatomy and function. The segmentation of cardiac images enables the quantification of essential parameters such as myocardial viability, ejection fraction, cardiac chamber volumes, and morphological features. These segmentation methods operate autonomously...
The number of CubeSats launched for data-intensive applications is increasing due to the modularity and reduced cost these platforms provide. Consequently, there is a growing need for efficient data processing and compression. Tailoring onboard processing with Machine Learning to specific mission tasks can optimise downlink usage by focusing only on relevant data, ultimately reducing the...
In the presentation, the introduction of the Intel FPGA AI Suite alongside the revolutionary AI Tensor Blocks recently incorporated into the latest FPGA device families by Intel for deep learning inference is showcased. These innovative FPGA components bring real-time, low-latency, and energy-efficient processing to the forefront. They are supported by the inherent advantages of Intel FPGAs,...
