Collaborative Research: SHF: Small: Integrating Synthesis and Optimization in Satisfiability Modulo Theories

对优化的大规模计算系统不断增长的需求给实现自动化设计优化和合成工具的重大改进带来了压力。该项目旨在通过优化和综合算法的更紧密集成，利用现代自动推理系统的进步，克服先前方法的局限性。该项目展示了基于可满足模理论（SMT）的综合与优化模理论（OMT）的新兴研究相结合的力量。该项目探索了理论和实践的进步，将研究演示集成到CVC4中，CVC4是目前唯一提供合成功能的SMT求解器。特别是，CVC4正在扩展优化功能，使其成为第一个能够同时执行合成和优化的自动推理工具。由此产生的可扩展框架支持新兴技术的系统设计，这些技术通常需要对非布尔（和混合）原语进行推理。该方法的可扩展性和通用性为综合和优化的交叉研究指明了一个新的方向，导致更可扩展和更复杂的系统设计，并允许在更广泛的目标类别上进行优化，包括安全性和安全性。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

The ever-increasing demand for optimized large-scale computing systems puts pressure on attaining significant improvements in automated design optimization and synthesis tools. This project aims to overcome the limitations of prior approaches through a tighter integration of optimization and synthesis algorithms leveraging advances in modern automated reasoning systems. The project demonstrates the power of Satisfiability Modulo Theories (SMT)-based synthesis combined with emerging research in Optimization Modulo Theories (OMT). The project explores advances in both theory and practice, with research demonstrations integrated into CVC4, the only SMT solver today providing synthesis features. In particular, CVC4 is being extended with optimization capabilities, making it the first automated reasoning tool capable of performing synthesis and optimization together. The resulting extensible framework enables system design for emerging technologies which often require reasoning over non Boolean (and mixed) primitives. The extensibility and generality of the approach charts a new direction of research at the intersection of synthesis and optimization, leads towards more scalable and complex system design, and allows optimization over a broader class of objectives including security and safety.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.