PQuantML: A Tool for End-to-End Hardware-aware Model Compression

• URL: http://arxiv.org/abs/2603.26595v1
• Date: Fri, 27 Mar 2026 16:54:46 GMT
• Title: PQuantML: A Tool for End-to-End Hardware-aware Model Compression
• Abstract summary: PQuantML is a hardware-aware neural network model compression library tailored to end-to-end environments.<n>The library implements multiple pruning methods with different granularities, as well as fixed-point quantization with support for High-Granularity Quantization.
• Score: 4.411331461983092
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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. 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.

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