SciPost Submission Page
BitHEP --- The Limits of Low-Precision ML in HEP
by Claudius Krause, Daohan Wang, Ramon Winterhalder
Submission summary
| Authors (as registered SciPost users): | Claudius Krause · Daohan Wang · Ramon Winterhalder |
| Submission information | |
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| Preprint Link: | scipost_202505_00053v1 (pdf) |
| Code repository: | https://github.com/ramonpeter/hep-bitnet/tree/main |
| Date submitted: | May 26, 2025, 2:01 p.m. |
| Submitted by: | Krause, Claudius |
| Submitted to: | SciPost Physics |
| Ontological classification | |
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| Academic field: | Physics |
| Specialties: |
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| Approach: | Computational |
Abstract
The increasing complexity of modern neural network architectures demands fast and memory-efficient implementations to mitigate computational bottlenecks. In this work, we evaluate the recently proposed Bitnet architecture in HEP applications, assessing its performance in classification, regression, and generative modeling tasks. Specifically, we investigate its suitability for quark-gluon discrimination, SMEFT parameter estimation, and detector simulation, comparing its efficiency and accuracy to state-of-the-art methods. Our results show that while Bitnet consistently performs competitively in classification tasks, its performance in regression and generation varies with the size and type of the network, highlighting key limitations and potential areas for improvement.
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