CARVE: Breaking Data Barriers in Chip Placement by Harnessing Reusable Expertise

2026-09-27

AI Summary

This study addresses the challenges of missing source data and catastrophic forgetting during fine-tuning when transferring pretrained chip placement models across designs. We propose CARVE, a continual adaptation framework that enables secure knowledge reuse through frozen base policies, immutable expert modules, and task credential mechanisms. Furthermore, we introduce a novel local-validation-based expert selection strategy, establishing theoretical bounds for expected performance guarantees under fixed distributions and worst-case sample complexity with bounded loss. Experimental results demonstrate that our method reduces training time by 58.5% while preserving nearly lossless HPWL gains. Notably, on IBM circuits, CARVE achieves an average 5.76% HPWL improvement without requiring any receiver-side training.

Abstract

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.

Problem

• Chip Placement
• Macro Placement
• Continual Adaptation
• Knowledge Reuse
• Catastrophic Forgetting

Innovation

• Continual Adaptation
• Macro Placement
• Knowledge Reuse
• Performance Guarantee
• Transfer Learning