Fast Machine Learning for Science Conference 2024

Session

Contributed talks

15 Oct 2024, 14:00

Steward Center 306 (Third floor) (Purdue University)

Description

13 mins + 2 mins Q&A

Presentation materials

ML Challenge website

ML Challenge website

90. [Remote] Randomized Point Serialization-Based Efficient Point Transformer in High-Energy Physics Applications

Siqi Miao (Georgia Tech)

15/10/2024, 14:00

Standard 15 min talk

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...

41. A Streamlined Neural Model for Real-Time Analysis at the First Level of the LHCb Trigger

Jiahui Zhuo (Univ. of Valencia and CSIC (ES))

15/10/2024, 14:15

Standard 15 min talk

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...

136. ML4GW: An all-encompassing software framework for real-time deep learning applications to search for gravitational waves

Will Benoit

15/10/2024, 14:30

Standard 15 min talk

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...

138. SONIC: A Portable framework for as-a-service ML serving

Dmitry Kondratyev (Purdue University (US))

15/10/2024, 14:45

Standard 15 min talk

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,...

69. Deep(er)RICH: Reconstruction of Imaging CherenkovDetectors with Swin Transformers and Normalizing Flow Models

James Giroux (W&M)

15/10/2024, 15:00

Standard 15 min talk

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)...

132. [Remote] Visualizing Loss Landscapes for Scientific Edge Machine Learning

Caleb Geniesse (Lawrence Berkeley National Laboratory)

15/10/2024, 17:00

Standard 15 min talk

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...

124. GWAK: Low-Latency Machine Learning for Real-Time Detection of Unmodeled Gravitational Wave Transients

Eric Anton Moreno (Massachusetts Institute of Technology (US))

15/10/2024, 17:15

Standard 15 min talk

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...

66. Realtime Anomaly Detection in the CMS Experiment

Noah Alexander Zipper (University of Colorado Boulder (US))

15/10/2024, 17:30

Standard 15 min talk

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...

117. Active Machine Learning for Projection Multi-photon 3D Printing

Mr Jason Edward Johnson (Purdue University)

15/10/2024, 17:45

Standard 15 min talk

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...

50. [Remote] Rapid, High-Resolution Coherent Diffractive Imaging with Physics-Informed Machine Learning

Oliver Hoidn

15/10/2024, 18:00

Standard 15 min talk

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 methods’ demand for extensive labeled training...

65. Fast Data, Faster Science: Connecting Instruments to Real-Time AI Compute

Denis Leshchev

16/10/2024, 16:45

Standard 15 min talk

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 edge—directly alongside the scientific instruments. By integrating these instruments with...

137. An end-to-end ML-enabled platform for precision neuroscience

Emadeldeen Hamdan (University of Illinois Chicago)

16/10/2024, 17:00

Standard 15 min talk

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...

129. Artificial Brains for Artificial Intelligence: A Novel Neurophysically Inspired Neural Network

Lorenzo Cacciapuoti

16/10/2024, 17:15

Standard 15 min talk

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...

109. Smart Pixels: Towards radiation hard ASIC with on-chip machine learning in 28nm CMOS

Ms Jieun Yoo (UIC)

16/10/2024, 17:30

Standard 15 min talk

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)....

121. Bit-Width Optimization of Power-Efficient Hardware Accelerators for Neural Networks using Catapult AI NN

Marzieh Vaez Torshizi (Siemens EDA)

16/10/2024, 17:45

Standard 15 min talk

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...

116. End-to-end workflow for ML-based qubit readout with QICK + hls4ml

Botao Du (Purdue University)

16/10/2024, 18:00

Standard 15 min talk

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...

51. rule4ml: An Open-Source Tool for Resource Utilization and Latency Estimation for ML Models on FPGA

Hamza Ezzaoui Rahali (University of Sherbrooke)

17/10/2024, 13:10

Standard 15 min talk

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...

96. Large Neural Network Partitionning for Distributed Inference on FPGAs

Charles-Étienne Granger (Université de Sherbrooke)

17/10/2024, 13:25

Standard 15 min talk

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 m² detector surface, are generating massive amounts of data across a large quantity of channels that increase hardware, energy and environmental costs....

111. Accelerating Reproducible FPGA Machine Learning Research With a Workflow Management Framework

Alexis Shuping (Northwestern University)

17/10/2024, 13:40

Standard 15 min talk

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...

49. Differentiable Weightless Neural Networks

Alan T. L. Bacellar (University of Texas at Austin)

17/10/2024, 13:55

Standard 15 min talk

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...

103. [Remote] Machine Learning Inference on FPGAs Using HLS4ML with oneAPI Backend

Haoyan Wang (Intel Corporation)

17/10/2024, 14:10

Standard 15 min talk

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...

64. [Remote] BRAM-Aware Quantization for Efficient Transformer Inference via Tile-based Architecture on a FPGA

Ling-Chi Yang (Institute of Electronics in National Yang Ming Chiao Tung University)

17/10/2024, 14:30

Standard 15 min talk

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...

122. Episodic reinforcement learning for 0νββ decay signal discrimination

Sonata Simonaitis-boyd

17/10/2024, 14:45

Standard 15 min talk

Neutrinoless double beta ($0 \nu \beta \beta$) decay is a Beyond the Standard Model process that, if discovered, could prove the Majorana nature of neutrinos—that 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...

91. Towards a machine learning trigger for high-purity germanium spectrometers

Janina Dorin Hakenmueller (Duke University)

17/10/2024, 15:00

Standard 15 min talk

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...

27. Lightning talk