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

对优化的大规模计算系统的不断增长的需求给在自动化设计、优化和综合工具方面取得重大改进带来了压力。该项目旨在利用现代自动推理系统的进步，通过更紧密地整合优化和合成算法，来克服以前方法的局限性。该项目展示了基于可满足性模理论(SMT)的综合的力量，并结合优化模理论(OMT)的新兴研究。该项目探索了理论和实践方面的进步，并将研究演示集成到CVC4中，CVC4是目前唯一提供合成功能的SMT解算器。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综合和优化的自动推理工具。由此产生的可扩展框架支持针对新兴技术的系统设计，这些技术通常需要在非布尔(和混合)原语上进行推理。该方法的可扩展性和通用性在综合和优化的交叉点上描绘了一个新的研究方向，导致了更可扩展和更复杂的系统设计，并允许在包括安全和安全在内的更广泛类别的目标上进行优化。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势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.