New FQTree algorithm optimizes boosted decision trees for hardware deployment

Researchers have developed FQTree, a novel algorithm for fine-grained quantization-aware training of boosted decision trees (BDTs). This method, coupled with the QXGB framework for automatic hardware generation, enables more efficient deployment of BDTs in latency-critical applications. FQTree's hardware-oriented leaf-value quantization scheme reduces LUT usage by 26-57% compared to existing FPGA-based BDT designs while maintaining or improving accuracy on benchmarks like JSC, MNIST, and NID. AI

IMPACT Enables more efficient hardware deployment of boosted decision trees, potentially reducing costs and improving performance in latency-critical applications.

RANK_REASON The cluster contains an academic paper detailing a new algorithm and framework for optimizing machine learning models for hardware. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

• field-programmable gate array
• FQTree
• MNIST database
• QXGB

• paper
• infra

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