research
∙
05/06/2023
Symbolic Regression on FPGAs for Fast Machine Learning Inference
The high-energy physics community is investigating the feasibility of de...
share
research
∙
12/09/2022
FAIR AI Models in High Energy Physics
The findable, accessible, interoperable, and reusable (FAIR) data princi...
research
∙
09/30/2022
FAIR for AI: An interdisciplinary, international, inclusive, and diverse community building perspective
A foundational set of findable, accessible, interoperable, and reusable ...
research
∙
08/10/2022
Neural Embedding: Learning the Embedding of the Manifold of Physics Data
In this paper, we present a method of embedding physics data manifolds w...
research
∙
07/01/2022
Ultra-low latency recurrent neural network inference on FPGAs for physics applications with hls4ml
Recurrent neural networks have been shown to be effective architectures ...
research
∙
05/16/2022
Real-time semantic segmentation on FPGAs for autonomous vehicles with hls4ml
In this paper, we investigate how field programmable gate arrays can ser...
research
∙
09/09/2020
GPU-accelerated machine learning inference as a service for computing in neutrino experiments
Machine learning algorithms are becoming increasingly prevalent and perf...
research
∙
08/08/2020
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 f...
research
∙
07/20/2020
GPU coprocessors as a service for deep learning inference in high energy physics
In the next decade, the demands for computing in large scientific experi...
research
∙
03/11/2020
Compressing deep neural networks on FPGAs to binary and ternary precision with HLS4ML
We present the implementation of binary and ternary neural networks in t...
research
∙
02/05/2020
Fast inference of Boosted Decision Trees in FPGAs for particle physics
We describe the implementation of Boosted Decision Trees in the hls4ml l...
research
∙
04/16/2018
