Distributed and Parallel Computing Systems
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Abstract
Abstract
Abstract 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 (HGQ). We evaluate PQuantML on the jet substructure classification task, 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. We benchmark PQuantML’s pruning and fixed-point quantization against QKeras, and validate that its integrated HGQ algorithm implementation reproduces the standalone HGQ library results.
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@article{Niemi2026PQuantML,
title = {PQuantML: a tool for end-to-end hardware-aware model compression},
author = {Roope Oskari Niemi and Anastasiia Petrovych and Arghya Ranjan Das and Enrico Lupi and Chang Sun and Dimitrios Danopoulos and Marlon Joshua Helbing and M. Liu and S. J. Dittmeier and M. Kagan and Vladimir Lončar and M. Pierini},
journal = {Machine Learning Science and Technology},
year = {2026},
doi = {10.1088/2632-2153/ae94e3},
url = {https://doi.org/10.1088/2632-2153/ae94e3}
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