约束驱动的模型优化：现代机器学习系统中选择压缩和加速技术的行业框架
Constraint-Driven Model Optimization: An Industry Framework for Selecting Compression and Acceleration Techniques in Modern Machine Learning Systems
- Indian Institute of Science（印度科学研究所）
- EXL（EXL公司）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对机器学习系统模型优化，提出约束驱动的多目标工程决策框架，沿五个约束维度描述生产部署，综合文献经验收益并映射到操作约束，给出规范性决策框架及四个场景优化管道，将模型优化形式化为约束感知过程。
AI中文摘要：
机器学习系统在云、边缘和企业环境中的快速部署使模型优化成为系统工程的前沿。尽管有大量关于量化、剪枝、知识蒸馏、参数高效微调（PEFT）和推理时间优化的文献，但从业者往往通过启发式方法而非原则性方法来运用这些技术。我们认为优化应被制定为约束驱动的多目标工程决策，并引入一个统一框架，该框架沿数据可用性、延迟预算、内存预算、精度容忍度和再训练预算这五个相互作用的约束维度来描述任何生产部署。在此分类法基础上，我们综合研究文献中报告的经验收益并将其映射到操作约束而非算法类别上。为确保实际相关性，我们通过回顾近期文献选择针对关键部署瓶颈报告有可衡量改进的方法。我们提出一个规范性决策框架，并为四个代表性工业场景提供优化管道以在实践中说明它。据我们所知，这项工作首次结构化地尝试将模型优化形式化为一个约束感知的多目标工程过程，综合了来自研究文献的定量证据。
英文摘要：
The rapid deployment of machine learning systems across cloud, edge, and enterprise environments has brought model optimization to the forefront of systems-engineering. Despite a rich literature spanning quantization, pruning, knowledge distillation, parameter-efficient fine-tuning (PEFT), and inference-time optimization, practitioners are often left navigating these techniques through heuristics rather than principled methodology. We argue that optimization should be formulated as a constraint-driven, multi-objective engineering decision and introduce a unified framework that characterizes any production deployment along five interacting constraint dimensions: data availability, latency budget, memory budget, accuracy tolerance, and retraining budget. Building on this taxonomy, we synthesize empirical gains reported across the research literature and map them to operational constraints rather than algorithmic categories. To ensure practical relevance, we selected these techniques by reviewing recent literature for methods that report measurable improvements against critical deployment bottlenecks. We propose a prescriptive decision framework and provide optimization pipelines for four representative industrial scenarios to illustrate it in practice. To the best of our knowledge, this work provides one of the first structured attempts to formalize model optimization as a constraint-aware, multi-objective engineering process, synthesizing quantitative evidence from the research literature.
