发表机构
Fudan University（复旦大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
GoalEvolve是一个目标驱动的物理设计算法演化框架，通过LLM教师与并行学生智能体协同，在8个ASAP7设计及Codex目标模式下，显著提升了TNS并降低了功耗。
AI 中文摘要
物理设计算法在紧密耦合的多阶段优化流程中运行，阶段局部的增益可能会消失或引发下游性能下降。现有的程序演化框架通常依赖阶段局部目标或无差异的多指标反馈，既无法保证最终结果更优，也无法确定应指导下一次迭代的未满足需求。我们提出GoalEvolve，这是一个目标驱动的框架，可使物理设计算法演化对完整流程的最终结果质量（QoR）负责。给定一个多目标QoR目标区域，GoalEvolve将未满足的需求转换为标准化的目标差距，识别主导瓶颈，并使用阶段解析的检查点证据定位负责的阶段。基于大语言模型（LLM）的教师随后将搜索范围缩小到相关的算法决策和源区域，而并行的学生智能体则通过完整流程评估来实现和验证假设。局部效应、优化债务和下游保留被保留为后续演化的机制证据。在8个ASAP7设计中，与默认的OpenROAD相比，GoalEvolve将布线后总负斜率偏差（TNS）平均提高了30.67%，并将泄漏功率和动态功率分别降低了21.18%和9.42%。相对于商业工具的目标，在以功率为主的设计上，它缩小了62.20%的标准化功率差距；在以时序为主的设计上，它超过了TNS目标；在联合设计上，它缩小了32.48%的等权重时序-功率差距。在匹配预算下针对Codex目标模式评估的所有3种设计中，GoalEvolve进一步将TNS提高了26.46%，同时分别将泄漏功率和动态功率降低了12.38%和0.76%。
英文摘要
Physical design algorithms operate within tightly coupled, multi-stage optimization flows, where stage-local gains may vanish or induce downstream degradation. Existing program-evolution frameworks often rely on stage-local objectives or undifferentiated multi-metric feedback, which neither guarantee better final results nor identify which unmet requirement should guide the next iteration. We present GoalEvolve, a goal-driven framework that makes physical design algorithm evolution accountable for the final quality of results (QoR) of the complete flow. Given a multi-objective QoR target region, GoalEvolve converts unmet requirements into normalized target gaps, identifies the dominant bottleneck, and uses stage-resolved checkpoint evidence to locate the responsible stage. An LLM-based Teacher then narrows the search to a relevant algorithmic decision and source region, while parallel Student agents implement and validate hypotheses through full-flow evaluation. Local effects, optimization debt, and downstream retention are retained as mechanism evidence for subsequent evolution. Across eight ASAP7 designs, GoalEvolve improves post-route TNS by 30.67% on average and reduces leakage and dynamic power by 21.18% and 9.42% versus default OpenROAD. Relative to commercial-tool goals, it closes 62.20% of the normalized power gap on power-dominant designs, surpasses the TNS goals on both timing-dominant designs, and closes 32.48% of the equal-weight timing-power gap on joint designs. Across all three designs evaluated against Codex goal mode under matched budgets, GoalEvolve further improves TNS by 26.46% while reducing leakage and dynamic power by 12.38% and 0.76%, respectively.
