Search-Optimized Quantization in Biomedical Ontology Alignment

TL;DR

This paper presents a search-optimized quantization method for biomedical ontology alignment using transformer models, achieving significant speed-up and memory reduction while maintaining performance.

Contribution

It introduces a systematic approach combining semantic similarity, search for optimal execution providers, and dynamic quantization for biomedical ontology alignment.

Findings

Achieved a 20x inference speed-up.

Reduced memory usage by approximately 70%.

Set new state-of-the-art results on DEFT 2020 tasks.

Abstract

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%.