arXiv:2607.04758v1 Announce Type: new Abstract: Physical design quality-of-results~(QoR) optimization is hard and expensive. Choices made at one stage can help or hurt later stages. Each evaluation requires a costly EDA run through the full flow. While existing methods still treat optimization as flat parameter tuning or a LLM-based script generation task, we present AgenticPD, a stage-aware agentic framework for physical design QoR optimization. Instead of re-running the full flow after every trial, AgenticPD is organized around the stage boundaries of the physical design flow, where a Judge
The increasing complexity and cost of chip design necessitate more efficient and autonomous optimization methods, making agentic AI approaches a timely solution.
This development indicates a significant step towards autonomous tools streamlining the critical and resource-intensive physical design phase of semiconductor manufacturing, impacting both cost and time to market.
Traditional, flat parameter tuning for physical design optimization is being superseded by stage-aware, agentic frameworks that can intelligently navigate complex design flows.
- · EDA software companies
- · Semiconductor design houses
- · AI agents developers
- · Cloud computing providers
- · Manual physical design engineers (routine tasks)
- · Legacy EDA tool developers (resistant to AI integration)
Optimization cycle times and costs for chip design will decrease significantly.
This could accelerate the development and release of new, more complex semiconductor technologies.
Increased efficiency in chip design might lower the barrier to entry for new hardware startups, fostering further innovation in specialized AI hardware.
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Read at arXiv cs.AI