Search Results for author:
Found 45 papers, 22 papers with code
Compressing deep neural networks on FPGAs to binary and ternary precision with HLS4ML
We discuss the trade-off between model accuracy and resource consumption.
Fast convolutional neural networks on FPGAs with hls4ml
We introduce an automated tool for deploying ultra low-latency, low-power deep neural networks with convolutional layers on FPGAs.
hls4ml: An Open-Source Codesign Workflow to Empower Scientific Low-Power Machine Learning Devices
Accessible machine learning algorithms, software, and diagnostic tools for energy-efficient devices and systems are extremely valuable across a broad range of application domains.
Fast inference of deep neural networks in FPGAs for particle physics
For our example jet substructure model, we fit well within the available resources of modern FPGAs with a latency on the scale of 100 ns.
MLPerf Tiny Benchmark
Advancements in ultra-low-power tiny machine learning (TinyML) systems promise to unlock an entirely new class of smart applications.
QONNX: Representing Arbitrary-Precision Quantized Neural Networks
We present extensions to the Open Neural Network Exchange (ONNX) intermediate representation format to represent arbitrary-precision quantized neural networks.
Fast inference of Boosted Decision Trees in FPGAs for particle physics
We describe the implementation of Boosted Decision Trees in the hls4ml library, which allows the translation of a trained model into FPGA firmware through an automated conversion process.
MLPF: Efficient machine-learned particle-flow reconstruction using graph neural networks
In general-purpose particle detectors, the particle-flow algorithm may be used to reconstruct a comprehensive particle-level view of the event by combining information from the calorimeters and the trackers, significantly improving the detector resolution for jets and the missing transverse momentum.
Particle Cloud Generation with Message Passing Generative Adversarial Networks
We propose JetNet as a novel point-cloud-style dataset for the ML community to experiment with, and set MPGAN as a benchmark to improve upon for future generative models.
Evaluating generative models in high energy physics
There has been a recent explosion in research into machine-learning-based generative modeling to tackle computational challenges for simulations in high energy physics (HEP).
Generative Adversarial Network
Vocal Bursts Intensity Prediction
Charged particle tracking via edge-classifying interaction networks
Recent work has demonstrated that geometric deep learning methods such as graph neural networks (GNNs) are well suited to address a variety of reconstruction problems in high energy particle physics.
Graph Generative Adversarial Networks for Sparse Data Generation in High Energy Physics
We develop a graph generative adversarial network to generate sparse data sets like those produced at the CERN Large Hadron Collider (LHC).
Open-source FPGA-ML codesign for the MLPerf Tiny Benchmark
We present our development experience and recent results for the MLPerf Tiny Inference Benchmark on field-programmable gate array (FPGA) platforms.
Explaining machine-learned particle-flow reconstruction
The particle-flow (PF) algorithm is used in general-purpose particle detectors to reconstruct a comprehensive particle-level view of the collision by combining information from different subdetectors.
Do graph neural networks learn traditional jet substructure?
At the CERN LHC, the task of jet tagging, whose goal is to infer the origin of a jet given a set of final-state particles, is dominated by machine learning methods.
Locality-Sensitive Hashing-Based Efficient Point Transformer with Applications in High-Energy Physics
Our findings highlight the superiority of using locality-sensitive hashing (LSH), especially OR \& AND-construction LSH, in kernel approximation for large-scale point cloud data with local inductive bias.
Lorentz group equivariant autoencoders
There has been significant work recently in developing machine learning (ML) models in high energy physics (HEP) for tasks such as classification, simulation, and anomaly detection.
FPGA-accelerated machine learning inference as a service for particle physics computing
New heterogeneous computing paradigms on dedicated hardware with increased parallelization, such as Field Programmable Gate Arrays (FPGAs), offer exciting solutions with large potential gains.
Data Analysis, Statistics and Probability High Energy Physics - Experiment Computational Physics Instrumentation and Detectors
Ps and Qs: Quantization-aware pruning for efficient low latency neural network inference
We study various configurations of pruning during quantization-aware training, which we term quantization-aware pruning, and the effect of techniques like regularization, batch normalization, and different pruning schemes on performance, computational complexity, and information content metrics.
FPGAs-as-a-Service Toolkit (FaaST)
Computing needs for high energy physics are already intensive and are expected to increase drastically in the coming years.
Computational Physics Distributed, Parallel, and Cluster Computing High Energy Physics - Experiment Data Analysis, Statistics and Probability Instrumentation and Detectors
Particle Graph Autoencoders and Differentiable, Learned Energy Mover's Distance
We explore the use of graph-based autoencoders, which operate on jets in their "particle cloud" representations and can leverage the interdependencies among the particles within a jet, for such tasks.
Accelerated Charged Particle Tracking with Graph Neural Networks on FPGAs
We develop and study FPGA implementations of algorithms for charged particle tracking based on graph neural networks.
Machine Learning in High Energy Physics Community White Paper
In this document we discuss promising future research and development areas for machine learning in particle physics.
BIG-bench Machine Learning
Vocal Bursts Intensity Prediction
Distance-Weighted Graph Neural Networks on FPGAs for Real-Time Particle Reconstruction in High Energy Physics
Graph neural networks have been shown to achieve excellent performance for several crucial tasks in particle physics, such as charged particle tracking, jet tagging, and clustering.
Graph Neural Networks for Particle Tracking and Reconstruction
In this chapter, we recapitulate the mathematical formalism of GNNs and highlight aspects to consider when designing these networks for HEP data, including graph construction, model architectures, learning objectives, and graph pooling.
graph construction
High Energy Physics - Phenomenology
High Energy Physics - Experiment
Data Analysis, Statistics and Probability
A reconfigurable neural network ASIC for detector front-end data compression at the HL-LHC
We demonstrate that a neural network autoencoder model can be implemented in a radiation tolerant ASIC to perform lossy data compression alleviating the data transmission problem while preserving critical information of the detector energy profile.
A FAIR and AI-ready Higgs boson decay dataset
To enable the reusability of massive scientific datasets by humans and machines, researchers aim to adhere to the principles of findability, accessibility, interoperability, and reusability (FAIR) for data and artificial intelligence (AI) models.
Applications and Techniques for Fast Machine Learning in Science
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.
Graph Neural Networks for Charged Particle Tracking on FPGAs
The determination of charged particle trajectories in collisions at the CERN Large Hadron Collider (LHC) is an important but challenging problem, especially in the high interaction density conditions expected during the future high-luminosity phase of the LHC (HL-LHC).
Particle-based Fast Jet Simulation at the LHC with Variational Autoencoders
We study how to use Deep Variational Autoencoders for a fast simulation of jets of particles at the LHC.
Machine Learning for Particle Flow Reconstruction at CMS
The standard particle flow algorithm reconstructs stable particles based on calorimeter clusters and tracks to provide a global event reconstruction that exploits the combined information of multiple detector subsystems, leading to strong improvements for quantities such as jets and missing transverse energy.
Graph Neural Networks in Particle Physics: Implementations, Innovations, and Challenges
Where previously these sets of data have been formulated as series or image data to match the available machine learning architectures, with the advent of graph neural networks (GNNs), these systems can be learned natively as graphs.
Physics Community Needs, Tools, and Resources for Machine Learning
Machine learning (ML) is becoming an increasingly important component of cutting-edge physics research, but its computational requirements present significant challenges.
FastML Science Benchmarks: Accelerating Real-Time Scientific Edge Machine Learning
Applications of machine learning (ML) are growing by the day for many unique and challenging scientific applications.
Data Science and Machine Learning in Education
The growing role of data science (DS) and machine learning (ML) in high-energy physics (HEP) is well established and pertinent given the complex detectors, large data, sets and sophisticated analyses at the heart of HEP research.
FastStamp: Accelerating Neural Steganography and Digital Watermarking of Images on FPGAs
In this work, we design the first accelerator platform FastStamp to perform DNN based steganography and digital watermarking of images on hardware.
FAIR AI Models in High Energy Physics
The findable, accessible, interoperable, and reusable (FAIR) data principles provide a framework for examining, evaluating, and improving how data is shared to facilitate scientific discovery.
Progress towards an improved particle flow algorithm at CMS with machine learning
The particle-flow (PF) algorithm, which infers particles based on tracks and calorimeter clusters, is of central importance to event reconstruction in the CMS experiment at the CERN LHC, and has been a focus of development in light of planned Phase-2 running conditions with an increased pileup and detector granularity.
End-to-end codesign of Hessian-aware quantized neural networks for FPGAs and ASICs
We develop an end-to-end workflow for the training and implementation of co-designed neural networks (NNs) for efficient field-programmable gate array (FPGA) and application-specific integrated circuit (ASIC) hardware.
Differentiable Earth Mover's Distance for Data Compression at the High-Luminosity LHC
In this paper, we train a convolutional neural network (CNN) to learn a differentiable, fast approximation of the EMD and demonstrate that it can be used as a substitute for computing-intensive EMD implementations.
Low Latency Edge Classification GNN for Particle Trajectory Tracking on FPGAs
In-time particle trajectory reconstruction in the Large Hadron Collider is challenging due to the high collision rate and numerous particle hits.
Improved particle-flow event reconstruction with scalable neural networks for current and future particle detectors
Efficient and accurate algorithms are necessary to reconstruct particles in the highly granular detectors anticipated at the High-Luminosity Large Hadron Collider and the Future Circular Collider.
Induced Generative Adversarial Particle Transformers
In high energy physics (HEP), machine learning methods have emerged as an effective way to accurately simulate particle collisions at the Large Hadron Collider (LHC).
Neural Architecture Codesign for Fast Bragg Peak Analysis
We develop an automated pipeline to streamline neural architecture codesign for fast, real-time Bragg peak analysis in high-energy diffraction microscopy.
Sets are all you need: Ultrafast jet classification on FPGAs for HL-LHC
We study various machine learning based algorithms for performing accurate jet flavor classification on field-programmable gate arrays and demonstrate how latency and resource consumption scale with the input size and choice of algorithm.
