FQTree把提升决策树的量化做到硬件级，在FPGA上能省26-57%查找表，精度还不掉，做边缘部署的可以看看。
FQTree是一种针对提升决策树（BDT）的细粒度量化感知训练算法，配套QXGB框架可自动生成低延迟硬件实现。它采用硬件导向的叶值量化方案，结合全局量化步长与树级移位，实现紧凑的非负整数叶表示、受控裁剪和偏差折叠。该量化在训练过程中逐步应用，使后续树能适应已量化集成模型的误差。在JSC、MNIST和NID数据集上，相比现有FPGA BDT设计，FQTree将LUT使用量减少26%至57%，同时保持或提升准确率。
FQTree: Fine-grained Quantization and Hardware Generation of Boosted Decision Trees
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. This work presents the FQTree algorithm{https://github.com/ecs-bristol/FQTree} for fine-grained quantization-aware training of BDTs, together with the QXGB framework for automatic hardware generation. FQTree introduces a hardware-oriented leaf-value quantization scheme that uses a global quantization step together with a tree-wise shift, enabling compact non-negative integer leaf representations, controlled clipping/pruning, and bias folding to reduce datapath cost. This work further applies this quantization during boosting so that later trees adapt to the errors of the already-quantized ensemble, and then lowers the trained model into low-latency hardware implementations through a compiler-based flow. Results on JSC, MNIST, and NID show that our method reduces LUT usage by 26-57\% compared with the state-of-the-art FPGA-based BDT designs while matching or improving accuracy.