English(EN) FQTree: Fine-grained Quantization and Hardware Generation of Boosted Decision Trees

FQTree算法实现增强决策树的高效硬件部署

作者 PulseAugur 编辑部 · [1 个来源] · 2026-08-12 14:56

研究人员开发了FQTree，一种用于增强决策树（BDTs）的细粒度感知量化训练的新型算法。该方法与用于自动硬件生成的QXGB框架相结合，解决了在延迟敏感应用中高效部署BDTs的挑战。FQTree采用面向硬件的叶值量化方案，可降低硬件成本并保持或提高准确性，与现有设计相比，在FPGA上显著减少了LUT使用量。 AI

影响实现了增强决策树更高效的硬件部署，可能降低延迟敏感应用中的成本并提高性能。

排序理由该条目描述了一种用于优化机器学习模型硬件部署的新算法和框架，发表在研究论文中。[lever_c_demoted from research: ic=1 ai=1.0]

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Hugging Face Daily Papers TIER_1 English(EN) · 2026-08-12 14:56

FQTree：提升决策树的细粒度量化与硬件生成

Boosted decision trees (BDTs) are widely used in latency-critical applications, but efficient hardware deployment remains challenging. Existing designs often rely on uniform or manually tuned fixed-point formats, which can introduce unnecessary hardware cost or accuracy loss. Thi…

本文如何被排名

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FQTree：提升决策树的细粒度量化与硬件生成

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