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Found 12 papers, 8 papers with code
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
Compressing deep neural networks on FPGAs to binary and ternary precision with HLS4ML
We discuss the trade-off between model accuracy and resource consumption.
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.
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
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.
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.
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.
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.
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.
Interpretable Geometric Deep Learning via Learnable Randomness Injection
LRI-induced models, once trained, can detect the points in the point cloud data that carry information indicative of the prediction label.
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.
