Applications and Techniques for Fast Machine Learning in Science

Abstract

In this community review report, we discuss applications and techniques for fast machine learning (ML) in science -- the concept of integrating power ML methods into the real-time experimental data processing loop to accelerate scientific discovery. The material for the report builds on two workshops held by the Fast ML for Science community and covers three main areas: applications for fast ML across a number of scientific domains; techniques for training and implementing performant and resource-efficient ML algorithms; and computing architectures, platforms, and technologies for deploying these algorithms. We also present overlapping challenges across the multiple scientific domains where common solutions can be found. This community report is intended to give plenty of examples and inspiration for scientific discovery through integrated and accelerated ML solutions. This is followed by a high-level overview and organization of technical advances, including an abundance of pointers to source material, which can enable these breakthroughs.

66 pages, 13 figures, 5 tables

References in corpus (66)

• Distilling the Knowledge in a Neural Network
• Observation of a new particle in the search for the Standard Model Higgs boson with the ATLAS detector at the LHC
• Observation of a new boson at a mass of 125 GeV with the CMS experiment at the LHC
• GW170817: Observation of Gravitational Waves from a Binary Neutron Star Inspiral
• Advanced Virgo: a 2nd generation interferometric gravitational wave detector
• Neural Architecture Search with Reinforcement Learning
• Multi-messenger Observations of a Binary Neutron Star Merger
• FitNets: Hints for Thin Deep Nets
• Photonics for artificial intelligence and neuromorphic computing
• FINN: A Framework for Fast, Scalable Binarized Neural Network Inference
• Particle-flow reconstruction and global event description with the CMS detector
• Baryon Acoustic Oscillations in the Lyα forest of BOSS DR11 quasars
• Trained Ternary Quantization
• The State of Sparsity in Deep Neural Networks
• Candidate Electromagnetic Counterpart to the Binary Black Hole Merger Gravitational Wave Event S190521g
• The morphology of HII regions during reionization
• Gravity Spy: Integrating Advanced LIGO Detector Characterization, Machine Learning, and Citizen Science
• Supernova Neutrino Detection
• Data-Free Knowledge Distillation for Deep Neural Networks
• First Season MWA EoR Power Spectrum Results at Redshift 7
• Science Requirements and Detector Concepts for the Electron-Ion Collider: EIC Yellow Report
• Apprentice: Using Knowledge Distillation Techniques To Improve Low-Precision Network Accuracy
• Improving Neural Network Quantization without Retraining using Outlier Channel Splitting
• Machine Learning for the Zwicky Transient Facility
• Machine-learning non-stationary noise out of gravitational wave detectors
• Fast convolutional neural networks on FPGAs with hls4ml
• Fink, a new generation of broker for the LSST community
• SNEWS 2.0: A Next-Generation SuperNova Early Warning System for Multi-messenger Astronomy
• Application of Quantum Machine Learning using the Quantum Kernel Algorithm on High Energy Physics Analysis at the LHC
• Zero-Shot Knowledge Distillation in Deep Networks
• Noise Reduction in Gravitational-wave Data via Deep Learning
• All-silicon light-emitting diodes waveguide-integrated with superconducting single-photon detectors
• Synchronization dynamics on the picosecond timescale in coupled Josephson junction neurons
• A New Method to Observe Gravitational Waves emitted by Core Collapse Supernovae
• MLPF: Efficient machine-learned particle-flow reconstruction using graph neural networks
• A Compiler Infrastructure for Accelerator Generators
• Charged particle tracking via edge-classifying interaction networks
• Ps and Qs: Quantization-aware pruning for efficient low latency neural network inference
• A Review of Basic Energy Reconstruction Techniques in Liquid Xenon and Argon Detectors for Dark Matter and Neutrino Physics Using NEST
• Vertex and Energy Reconstruction in JUNO with Machine Learning Methods
• Graph Neural Networks for Particle Tracking and Reconstruction
• A Convolutional Neural Network for Multiple Particle Identification in the MicroBooNE Liquid Argon Time Projection Chamber
• Technical Design Report (TDR): Searching for a Sterile Neutrino at J-PARC MLF (E56, JSNS2)
• PySINDy: A Python package for the Sparse Identification of Nonlinear Dynamics from Data
• Efficient Monte Carlo Integration Using Boosted Decision Trees and Generative Deep Neural Networks
• Accelerated Charged Particle Tracking with Graph Neural Networks on FPGAs
• LHC analysis-specific datasets with Generative Adversarial Networks
• Exploring particle dynamics during self-organization processes via rotationally invariant latent representations
• EigenDamage: Structured Pruning in the Kronecker-Factored Eigenbasis
• GPU-accelerated machine learning inference as a service for computing in neutrino experiments
• Accelerating Recurrent Neural Networks for Gravitational Wave Experiments
• FPGAs-as-a-Service Toolkit (FaaST)
• Same, Same But Different - Recovering Neural Network Quantization Error Through Weight Factorization
• HAWQV3: Dyadic Neural Network Quantization
• I-BERT: Integer-only BERT Quantization
• The Mu2e experiment at Fermilab: a search for lepton flavor violation
• 4K-Memristor Analog-Grade Passive Crossbar Circuit
• Reservoir Computing with Superconducting Electronics
• Scalable, End-to-End, Deep-Learning-Based Data Reconstruction Chain for Particle Imaging Detectors
• Cosmic Background Removal with Deep Neural Networks in SBND
• Real-time Artificial Intelligence for Accelerator Control: A Study at the Fermilab Booster
• Data Augmentation at the LHC through Analysis-specific Fast Simulation with Deep Learning
• Hessian-Aware Pruning and Optimal Neural Implant
• GPU acceleration of a patient-specific airway image segmentation and its assessment
• Instance Segmentation GNNs for One-Shot Conformal Tracking at the LHC
• PAR: Personal Activity Radius Camera View for Contextual Sensing

Cited by in corpus (12)

• Machine Learning in Nuclear Physics
• MLGWSC-1: The first Machine Learning Gravitational-Wave Search Mock Data Challenge
• Deep Learning Detection and Classification of Gravitational Waves from Neutron Star-Black Hole Mergers
• Ephemeral Learning -- Augmenting Triggers with Online-Trained Normalizing Flows
• Machine learning light hypernuclei
• Localization of gravitational waves using machine learning
• Predicting Solid State Material Platforms for Quantum Technologies
• Neural network accelerator for quantum control
• Embedded FPGA Developments in 130nm and 28nm CMOS for Machine Learning in Particle Detector Readout
• Low latency optical-based mode tracking with machine learning deployed on FPGAs on a tokamak
• Development of a resource-efficient FPGA-based neural network regression model for the ATLAS muon trigger upgrades
• Variational Autoencoders for At-Source Data Reduction and Anomaly Detection in High Energy Particle Detectors