PQuantML: A Tool for End-to-End Hardware-aware Model Compression
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 pro...
| Authors | Roope Niemi, Anastasiia Petrovych, Arghya Ranjan Das et al. |
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| Primary category | cs.LG |
| Published | 27 March 2026 |
| Last updated at source | 27 March 2026 |
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