5 citations · 5 across the 12 of their papers we have counts for
14 papers
WARP Logic Neural Networks
Lino Gerlach, Thore Gerlach, Liv Våge +2
Fast and efficient AI inference is increasingly important, and recent models that directly learn low-level logic operations have achieved state-of-the-art performance. However, exi…
hls4ml: A Flexible, Open-Source Platform for Deep Learning Acceleration on Reconfigurable Hardware
Jan-Frederik Schulte, Benjamin Ramhorst, Chang Sun +50
We present hls4ml, a free and open-source platform that translates machine learning (ML) models from modern deep learning frameworks into high-level synthesis (HLS) code that can b…
WARP-LUTs -- Walsh-Assisted Relaxation for Probabilistic Look Up Tables
Lino Gerlach, Liv Våge, Thore Gerlach +2
Fast and efficient machine learning is of growing interest to the scientific community and has spurred significant research into novel model architectures and hardware-aware design…
Evaluation of Novel Fast Machine Learning Algorithms for Knowledge-Distillation-Based Anomaly Detection at CMS
Lino Gerlach, Elliott Kauffman, Abhishikth Mallampalli
The CICADA (Calorimeter Image Convolutional Anomaly Detection Algorithm) project aims to detect anomalous physics signatures without bias from theoretical models in proton-proton c…
Measurement of Exclusive --argon Interactions Using ProtoDUNE-SP
DUNE Collaboration, S. Abbaslu, A. Abed Abud +1323
We present the measurement of --argon inelastic cross sections using the ProtoDUNE Single-Phase liquid argon time projection chamber in the incident kinetic energy ran…
Rapid Inference of Logic Gate Neural Networks for Anomaly Detection in High Energy Physics
Lino Gerlach, Elliott Kauffman, Liv Helen Våge +1
The increasing data rates and complexity of detectors at the Large Hadron Collider (LHC) necessitate fast and efficient machine learning models, particularly for rapid selection of…