Vladimir Loncar

Orcid: 0000-0003-3651-0232

According to our database1, Vladimir Loncar authored at least 33 papers between 2016 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

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Links

On csauthors.net:

Bibliography

2024
Tailor: Altering Skip Connections for Resource-Efficient Inference.
ACM Trans. Reconfigurable Technol. Syst., March, 2024

Sets are all you need: Ultrafast jet classification on FPGAs for HL-LHC.
CoRR, 2024

Ultra Fast Transformers on FPGAs for Particle Physics Experiments.
CoRR, 2024

SymbolNet: Neural Symbolic Regression with Adaptive Dynamic Pruning.
CoRR, 2024

2023
Ultra-low latency recurrent neural network inference on FPGAs for physics applications with hls4ml.
Mach. Learn. Sci. Technol., June, 2023

Symbolic Regression on FPGAs for Fast Machine Learning Inference.
CoRR, 2023

Tailor: Altering Skip Connections for Resource-Efficient Inference.
CoRR, 2023

FPGA Resource-aware Structured Pruning for Real-Time Neural Networks.
Proceedings of the International Conference on Field Programmable Technology, 2023

Adapting Skip Connections for Resource-Efficient FPGA Inference.
Proceedings of the 2023 ACM/SIGDA International Symposium on Field Programmable Gate Arrays, 2023

2022
Real-time semantic segmentation on FPGAs for autonomous vehicles with hls4ml.
Mach. Learn. Sci. Technol., December, 2022

<i>AIgean</i>: An Open Framework for Deploying Machine Learning on Heterogeneous Clusters.
ACM Trans. Reconfigurable Technol. Syst., 2022

Author Correction: Autoencoders on field-programmable gate arrays for real-time, unsupervised new physics detection at 40 MHz at the Large Hadron Collider.
Nat. Mach. Intell., 2022

Autoencoders on field-programmable gate arrays for real-time, unsupervised new physics detection at 40 MHz at the Large Hadron Collider.
Nat. Mach. Intell., 2022

Lightweight jet reconstruction and identification as an object detection task.
Mach. Learn. Sci. Technol., 2022

QONNX: Representing Arbitrary-Precision Quantized Neural Networks.
CoRR, 2022

2021
Automatic heterogeneous quantization of deep neural networks for low-latency inference on the edge for particle detectors.
Nat. Mach. Intell., 2021

Compressing deep neural networks on FPGAs to binary and ternary precision with hls4ml.
Mach. Learn. Sci. Technol., 2021

Fast convolutional neural networks on FPGAs with hls4ml.
Mach. Learn. Sci. Technol., 2021

A reconfigurable neural network ASIC for detector front-end data compression at the HL-LHC.
CoRR, 2021

hls4ml: An Open-Source Codesign Workflow to Empower Scientific Low-Power Machine Learning Devices.
CoRR, 2021

Fast convolutional neural networks on FPGAs with hls4ml.
CoRR, 2021

Accelerating Recurrent Neural Networks for Gravitational Wave Experiments.
Proceedings of the 32nd IEEE International Conference on Application-specific Systems, 2021

2020
Distance-Weighted Graph Neural Networks on FPGAs for Real-Time Particle Reconstruction in High Energy Physics.
Frontiers Big Data, 2020

Accelerated Charged Particle Tracking with Graph Neural Networks on FPGAs.
CoRR, 2020

Ultra Low-latency, Low-area Inference Accelerators using Heterogeneous Deep Quantization with QKeras and hls4ml.
CoRR, 2020

Fast inference of Boosted Decision Trees in FPGAs for particle physics.
CoRR, 2020

AIgean: An Open Framework for Machine Learning on Heterogeneous Clusters.
Proceedings of the 28th IEEE Annual International Symposium on Field-Programmable Custom Computing Machines, 2020

2019
FPGA-Accelerated Machine Learning Inference as a Service for Particle Physics Computing.
Comput. Softw. Big Sci., December, 2019

C and Fortran OpenMP programs for rotating Bose-Einstein condensates.
Comput. Phys. Commun., 2019

Fast Inference of Deep Neural Networks for Real-time Particle Physics Applications.
Proceedings of the 2019 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays, 2019

2017
OpenMP GNU and Intel Fortran programs for solving the time-dependent Gross-Pitaevskii equation.
Comput. Phys. Commun., 2017

2016
OpenMP, OpenMP/MPI, and CUDA/MPI C programs for solving the time-dependent dipolar Gross-Pitaevskii equation.
Comput. Phys. Commun., 2016

CUDA programs for solving the time-dependent dipolar Gross-Pitaevskii equation in an anisotropic trap.
Comput. Phys. Commun., 2016


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