CARVE：通过利用可复用专业知识打破芯片布局中的数据壁垒
CARVE: Breaking Data Barriers in Chip Placement by Harnessing Reusable Expertise
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中文总结 AI 辅助
CARVE框架通过冻结基础策略、不可变专家和局部验证凭证，实现宏布局策略的持续复用，减少训练时间58.5%，并保持性能稳定。
中文摘要 AI 辅助
预训练的宏布局策略可以减少跨电路重复优化，但部署时若原始训练数据不可用，则常会暴露于不熟悉的设计。对单一服务模型反复微调可能会覆盖先前改进，而简单保存检查点无法确定其可靠复用的位置。我们提出“通过复用已验证专业知识进行持续适应”（CARVE）框架，该框架将累积的专业知识表示为冻结的基础策略、不可变专家以及通过局部验证获得的任务特定凭证。对于新任务，CARVE首先检查现有专家，仅当无专家合格时，才从冻结基础训练新专家。在固定任务分布和可控制累积误差的验证规则下，我们建立了重复复用的期望性能保证。对于有界损失，我们还推导了可靠复用所需局部样本的匹配最坏情况界限。在宏布局中，复用优先的后续操作将记录的训练时间减少58.5%（从9.66小时降至4.01小时），而平均HPWL增益仅从8.41%变为7.86%。在模拟接收部署中，导入的专家在六个（共七个）新IBM电路上被复用，无需接收端训练，实现了5.76%的平均HPWL增益。导航研究提供了关于修复保留和重复适应的补充证据。
英文摘要
Pretrained macro-placement policies can reduce repeated optimization across circuits, but deployment often exposes them to unfamiliar designs when the original training data are unavailable. Repeatedly fine-tuning a single serving model can overwrite earlier improvements, while simply saving checkpoints does not determine where they can be reliably reused. We introduce Continual Adaptation through the Reuse of Validated Expertise (CARVE), a framework that represents accumulated expertise as a frozen base policy, immutable specialists, and task-specific credentials obtained through local validation. For a new task, CARVE first checks existing specialists and trains a new specialist from the frozen base only when none qualifies. Under fixed task distributions and validation rules that control cumulative error, we establish expected-performance guarantees for repeated reuse. For bounded losses, we also derive matching worst-case bounds on the local samples needed for reliable reuse. In macro placement, a reuse-first follow-up reduces recorded training time by 58.5% (9.66 to 4.01 hours), while mean HPWL gain changes only from 8.41% to 7.86%. In a simulated receiving deployment, imported specialists are reused on six of seven new IBM circuits with no receiver-side training, achieving a 5.76% mean HPWL gain. Navigation studies provide complementary evidence on repair retention and repeated adaptation.
发表机构
- Purdue University（普渡大学）
- Columbia University（哥伦比亚大学）
机构由 AI 辅助整理，请以论文原文为准。
