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Fast Machine Learning for Science Conference 2025
Fast Machine Learning for Science Conference 2025

Sep 1 – 5, 2025
ETH Zurich
Europe/Zurich timezone

Event menu

  • Overview
  • Invited Speakers
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  • Contribution List
  • Book of Abstracts
  • Registration
  • How to become a Sponsor
  • Poster awards
  • Practical information
  • Conference photo

Local organisers

  • fml-2025-organisers@cern.ch

Details for Vladimir Loncar

CERN

Author in the following contributions

  • Designing and Deploying Low-Latency Neural Networks on FPGAs with HGQ and da4ml
  • Radiation-Hard, ML-Based, Low-Latency Compression for the LHCb ECAL Upgrade
  • End-to-End Neural Network Compression and Deployment for Hardware Acceleration Using PQuant and hls4ml
  • Pushing Matrix-Vector Multiplication Performance on AMD AI Engines for Low-Latency Edge Inference
  • SparsePixels: Efficient Convolution for Sparse Data on FPGAs
  • Accelerating Efficient Transformer Architectures for Point Cloud Data using hls4ml (REMOTE)
  • da4ml: Distributed Arithmetic for Real-time Neural Networks on FPGAs
  • KAN-LUT: Efficient LUT-Based Acceleration of Kolomogorov-Arnold Networks (KANs) on FPGAs
  • Evolution of the oneAPI backend for hls4ml
  • Integrating Support for Google XLS in hls4ml
  • wa-hls4ml: A Benchmark and Surrogate Models for hls4ml Resource and Latency Estimation
CERN
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