测试低精度神经网络在高能物理中的表现，发现分类任务可行，回归生成有局限。
BitHEP -- The Limits of Low-Precision ML in HEP
- 用BitNet模型做高能物理任务的低精度推理测试
- 分类任务表现接近顶尖方法，回归与生成任务效果随网络规模变化
- 适合对精度要求不高的分类场景，为模型压缩提供参考
现代神经网络架构日益复杂，亟需快速、低内存的实现以缓解计算瓶颈。本文评估了近期提出的BitNet架构在高能物理（HEP）应用中的表现，涵盖分类、回归和生成建模任务。具体考察其在夸克-胶子区分、SMEFT参数估计及探测器模拟中的适用性，并与当前最优方法比较效率与精度。结果表明，BitNet在分类任务中表现持续具有竞争力，但在回归与生成任务中性能随网络大小和类型波动，揭示了其关键限制与改进方向。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。