Description
13 mins + 2 mins Q&A
This study introduces a novel transformer model optimized for large-scale point cloud processing in scientific domains such as high-energy physics (HEP) and astrophysics. Addressing the limitations of graph neural networks and standard transformers, our model integrates local inductive bias and achieves near-linear complexity with hardware-friendly regular operations. One contribution of this...
One of the most significant challenges in tracking reconstruction is the reduction of "ghost tracks," which are composed of false hit combinations in the detectors. When tracking reconstruction is performed in real-time at 30 MHz, it introduces the difficulty of meeting high efficiency and throughput requirements. A single-layer feed-forward neural network (NN) has been developed and trained...
Deep Learning (DL) applications for gravitational wave (GW) physics are becoming increasingly common without the infrastructure to validate them at scale or deploy them in real-time. The challenge of gravitational waves requires and real-time time series workflow. With ever more sensitive GW observing runs beginning in 2023-5 and progressing through the next decade, ever-increasing...
Computing demands for large scientific experiments, including experiments at the Large Hadron Collider and the future DUNE neutrino detector, will increase dramatically in the next decades. Heterogeneous computing provides a solution enabling increased computing demands that pass the limitations brought on by the end of Dennard scaling. However, to effectively exploit Heterogeneous compute,...
The Deep(er)RICH architecture integrates Swin Transformers and normalizing flows, and demonstrates significant advancements in particle identification (PID) and fast simulation. Building on the earlier DeepRICH model, Deep(er)RICH extends its capabilities across the entire kinematic region covered by the DIRC detector in the \textsc{GlueX} experiment. It learns particle identification (PID)...
Characterizing the loss of a neural network can provide insights into local structure (e.g., smoothness of the so-called loss landscape) and global properties of the underlying model (e.g., generalization performance). Inspired by powerful tools from topological data analysis (TDA) for summarizing high-dimensional data, we are developing tools for characterizing the underlying shape (or...
Matched-filtering detection techniques for gravitational-wave (GW) signals in ground-based interferometers rely on having well-modeled templates of the GW emission. Such techniques have been traditionally used in searches for compact binary coalescences (CBCs) and have been employed in all known GW detections so far. However, interesting science cases aside from compact mergers do not yet have...
We present the development, deployment, and initial recorded data of an unsupervised autoencoder trained for unbiased detection of new physics signatures in the CMS experiment during LHC Run 3. The Global Trigger makes the final hardware decision to readout or discard data from each LHC collision, which occur at a rate of 40 MHz, within nanosecond latency constraints. The anomaly detection...
The rapidly developing frontiers of additive manufacturing, especially multi-photon lithography, create a constant need for optimization of new process parameters. Multi-photon lithography is a 3D printing technique which uses the nonlinear absorption of two or more photons from a high intensity light source to induce highly confined polymerization. The process can 3D print structures with...
Coherent diffractive imaging (CDI) techniques like ptychography enable nanoscale imaging, bypassing the resolution limits of lenses. Yet, the need for time consuming iterative phase recovery hampers real-time imaging. While supervised deep learning strategies have increased reconstruction speed, they sacrifice image quality. Furthermore, these methodsโ demand for extensive labeled training...
Modern scientific instruments generate vast amounts of data at increasingly higher rates, outpacing traditional data management strategies that rely on large-scale transfers to offline storage for post-analysis. To enable next-generation experiments, data processing must be performed at the edgeโdirectly alongside the scientific instruments. By integrating these instruments with...
In situ machine learning data processing for neuroscience probes can have wide-reaching applications from data filtering, event triggering, and ultimately real-time interventions at kilohertz frequencies intrinsic to natural systems. In this work, we present the integration of Machine Learning (ML) algorithms on an off-the-shelf neuroscience data acquisition platform by Spike Gadgets. The...
Artificial neural networks (ANNs) are capable of complex feature extraction and classification with applications in robotics, natural language processing, and data science. Yet, many ANNs have several key limitations; notably, current neural network architectures require enormous training datasets and are computationally inefficient. It has been posited that biophysical computations in single...
We introduce a smart pixel prototype readout integrated circuit (ROIC) fabricated using a 28 nm bulk CMOS process, which integrates a machine learning (ML) algorithm for data filtering directly within the pixel region. This prototype serves as a proof-of-concept for a potential Phase III pixel detector upgrade of the Compact Muon Solenoid (CMS) experiment at the Large Hadron Collider (LHC)....
Nowadays, the application of neural networks (NNs) has expanded across different industries (e.g., autonomous vehicles, manufacturing, natural-language processing, etc.) due to their improved accuracy results. This was made possible because of the increased complexity of these networks which requires higher computational efforts and memory consumption. As a result, there is more demand for...
High-fidelity single-shot quantum state readout is crucial for advancing quantum technology. Machine-learning (ML) assisted qubit-state discriminators have shown high readout fidelity and strong resistance to crosstalk. By directly integrating these ML models into FPGA-based control hardware, fast feedback control becomes feasible, which is vital for quantum error correction and other...
Deploying Machine Learning (ML) models on Field-Programmable Gate Arrays (FPGAs) is becoming increasingly popular across various domains as a low-latency and low-power solution that helps manage large data rates generated by continuously improving detectors. However, developing ML models for FPGA deployment is often hindered by the time-consuming synthesis procedure required to evaluate...
Ultra-high-speed detectors are crucial in scientific and healthcare fields, such as medical imaging, particle accelerators and astrophysics. Consequently, upcoming large dark matter experiments, like the ARGO detector with an anticipated 200 mยฒ detector surface, are generating massive amounts of data across a large quantity of channels that increase hardware, energy and environmental costs....
High-Level Synthesis (HLS) techniques, coupled with domain-specific translation tools such as HLS4ML, have made the development of FPGA-based Machine Learning (ML) accelerators more accessible than ever before, allowing scientists to develop and test new models on hardware with unprecedented speed. However, these advantages come with significant costs in terms of implementation complexity. The...
We introduce the Differentiable Weightless Neural Network (DWN), a model based on interconnected lookup tables. Training of DWNs is enabled by a novel Extended Finite Difference technique for approximate differentiation of binary values. We propose Learnable Mapping, Learnable Reduction, and Spectral Regularization to further improve the accuracy and efficiency of these models. We evaluate...
The increasing demand for efficient machine learning (ML) acceleration has intensified the need for user-friendly yet flexible solutions, particularly for edge computing. Field Programmable Gate Arrays (FPGAs), with their high configurability and low-latency processing, offer a compelling platform for this challenge. Our presentation gives update to an end-to-end ML acceleration flow utilizing...
Transformers are becoming increasingly popular in fields such as natural language processing, speech processing, and computer vision. However, due to the high memory bandwidth and power requirements of Transformers, contemporary hardware is gradually unable to keep pace with the trend of larger models. To improve hardware efficiency and increase throughput and reduce latency, there has been a...
Neutrinoless double beta ($0 \nu \beta \beta$) decay is a Beyond the Standard Model process that, if discovered, could prove the Majorana nature of neutrinosโthat they are their own antiparticles. In their search for this process, $0 \nu \beta \beta$ decay experiments rely on signal/background discrimination, which is traditionally approached as a supervised learning problem. However, the...
High-purity germanium spectrometers are widely used in fundamental physics and beyond. Their excellent energy resolution enables the detection of electromagnetic signals and recoils down to below 1keV ionization energy and even lower. However, the detectors are also very sensitive to all types of noise that will overwhelm the trigger routines of the data acquisition system and significantly...
