3 citations · 3 across the 7 of their papers we have counts for
4 papers · 1 filter
Reconfigurable On-Chip AI for Particle Physics Detectors with Logic Neural Networks
Mila Bileska, Lino Gerlach, Jennet Dickinson +4
On-device machine learning is increasingly important in applications where extreme data rates or resource constraints make centralized processing infeasible. Logic neural networks…
On-Detector Machine Learning for Beam-Induced Background Rejection at a 10 TeV Muon Collider
Daniel Abadjiev, Eliza Howard, Tsz Ngong You +35
A 10 TeV Muon Collider is a compelling candidate for a future energy-frontier facility, offering unprecedented opportunities to explore the fundamental laws of particle physics. Mu…
Machine Learning on Heterogeneous, Edge, and Quantum Hardware for Particle Physics (ML-HEQUPP)
Julia Gonski, Jennifer Ott, Jenni Ott +129
The next generation of particle physics experiments will face a new era of challenges in data acquisition, due to unprecedented data rates and volumes along with extreme environmen…
On-chip probabilistic inference for charged-particle tracking at the sensor edge
Arghya Ranjan Das, David Jiang, Rachel Kovach-Fuentes +31
Modern scientific instruments operate under increasingly extreme constraints on bandwidth, latency, and power. Inference at the sensor edge determines experimental data collection…