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/...
| Authors | Zhiqiang Que, Chang Sun, Haiyang Wang et al. |
|---|---|
| Primary category | cs.AR |
| Published | 12 August 2026 |
| Last updated at source | 12 August 2026 |
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