用搜索优化量化，让生物医学本体对齐模型快7倍、省70%内存
Search-Optimized Quantization in Biomedical Ontology Alignment
- 基于余弦相似度匹配医学术语与UMLS词典，结合ONNX运行时搜索最优执行路径
- 在DEFT 2020任务上实现新最佳性能，推理速度提升20倍，内存减少约70%
- 专为边缘设备设计，适合资源受限环境下的医疗知识图谱应用
在人工智能快速发展的背景下，大型模型的部署面临计算资源消耗大、内存占用高和延迟高等挑战。针对这一问题，本文提出一种系统性方法，利用监督学习的先进Transformer模型，基于余弦相似度计算生物医学通俗词汇与统一医学语言系统（UMLS Metathesaurus）之间的语义相似性，实现本体对齐。研究采用Microsoft Olive在ONNX Runtime后端上搜索最优执行提供者（EP），并结合Intel Neural Compressor与IPEX（Intel Extension for PyTorch）实施动态量化优化。在DEFT 2020评估活动的两项任务中，该方法取得新的最佳性能：平均推理速度提升20倍，内存使用量降低约70%，同时保持原有性能指标不变。
原文摘要 · Abstract (English)
In the fast-moving world of AI, as organizations and researchers develop more advanced models, they face challenges due to their sheer size and computational demands. Deploying such models on edge devices or in resource-constrained environments adds further challenges related to energy consumption, memory usage and latency. To address these challenges, emerging trends are shaping the future of efficient model optimization techniques. From this premise, by employing supervised state-of-the-art transformer-based models, this research introduces a systematic method for ontology alignment, grounded in cosine-based semantic similarity between a biomedical layman vocabulary and the Unified Medical Language System (UMLS) Metathesaurus. It leverages Microsoft Olive to search for target optimizations among different Execution Providers (EPs) using the ONNX Runtime backend, followed by an assembled process of dynamic quantization employing Intel Neural Compressor and IPEX (Intel Extension for PyTorch). Through our optimization process, we conduct extensive assessments on the two tasks from the DEFT 2020 Evaluation Campaign, achieving a new state-of-the-art in both. We retain performance metrics intact, while attaining an average inference speed-up of 20x and reducing memory usage by approximately 70%.
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