PQuantML: A Tool for End-to-End Hardware-aware Model Compression
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866915895358521344 |
|---|---|
| author | Niemi, Roope Petrovych, Anastasiia Das, Arghya Ranjan Lupi, Enrico Sun, Chang Danopoulos, Dimitrios Helbing, Marlon Joshua Liu, Mia Dittmeier, Sebastian Kagan, Michael Loncar, Vladimir Pierini, Maurizio |
| author_facet | Niemi, Roope Petrovych, Anastasiia Das, Arghya Ranjan Lupi, Enrico Sun, Chang Danopoulos, Dimitrios Helbing, Marlon Joshua Liu, Mia Dittmeier, Sebastian Kagan, Michael Loncar, Vladimir Pierini, Maurizio |
| contents | PQuantML is a new open-source, hardware-aware neural network model compression library tailored to end-to-end workflows. Motivated by the need to deploy performant models to environments with strict latency constraints, PQuantML simplifies training of compressed models by providing a unified interface to apply pruning and quantization, either jointly or individually. The library implements multiple pruning methods with different granularities, as well as fixed-point quantization with support for High-Granularity Quantization. We evaluate PQuantML on representative tasks such as the jet substructure classification, so-called jet tagging, an on-edge problem related to real-time LHC data processing. Using various pruning methods with fixed-point quantization, PQuantML achieves substantial parameter and bit-width reductions while maintaining accuracy. The resulting compression is further compared against existing tools, such as QKeras and HGQ. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_26595 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | PQuantML: A Tool for End-to-End Hardware-aware Model Compression Niemi, Roope Petrovych, Anastasiia Das, Arghya Ranjan Lupi, Enrico Sun, Chang Danopoulos, Dimitrios Helbing, Marlon Joshua Liu, Mia Dittmeier, Sebastian Kagan, Michael Loncar, Vladimir Pierini, Maurizio Machine Learning High Energy Physics - Experiment PQuantML is a new open-source, hardware-aware neural network model compression library tailored to end-to-end workflows. Motivated by the need to deploy performant models to environments with strict latency constraints, PQuantML simplifies training of compressed models by providing a unified interface to apply pruning and quantization, either jointly or individually. The library implements multiple pruning methods with different granularities, as well as fixed-point quantization with support for High-Granularity Quantization. We evaluate PQuantML on representative tasks such as the jet substructure classification, so-called jet tagging, an on-edge problem related to real-time LHC data processing. Using various pruning methods with fixed-point quantization, PQuantML achieves substantial parameter and bit-width reductions while maintaining accuracy. The resulting compression is further compared against existing tools, such as QKeras and HGQ. |
| title | PQuantML: A Tool for End-to-End Hardware-aware Model Compression |
| topic | Machine Learning High Energy Physics - Experiment |
| url | https://arxiv.org/abs/2603.26595 |