Reinforcement Learning Policy as Macro Regulator Rather than Macro Placer (2412.07167v1)

Abstract: In modern chip design, placement aims at placing millions of circuit modules, which is an essential step that significantly influences power, performance, and area (PPA) metrics. Recently, reinforcement learning (RL) has emerged as a promising technique for improving placement quality, especially macro placement. However, current RL-based placement methods suffer from long training times, low generalization ability, and inability to guarantee PPA results. A key issue lies in the problem formulation, i.e., using RL to place from scratch, which results in limits useful information and inaccurate rewards during the training process. In this work, we propose an approach that utilizes RL for the refinement stage, which allows the RL policy to learn how to adjust existing placement layouts, thereby receiving sufficient information for the policy to act and obtain relatively dense and precise rewards. Additionally, we introduce the concept of regularity during training, which is considered an important metric in the chip design industry but is often overlooked in current RL placement methods. We evaluate our approach on the ISPD 2005 and ICCAD 2015 benchmark, comparing the global half-perimeter wirelength and regularity of our proposed method against several competitive approaches. Besides, we test the PPA performance using commercial software, showing that RL as a regulator can achieve significant PPA improvements. Our RL regulator can fine-tune placements from any method and enhance their quality. Our work opens up new possibilities for the application of RL in placement, providing a more effective and efficient approach to optimizing chip design. Our code is available at \url{https://github.com/lamda-bbo/macro-regulator}.

• The paper introduces a novel RL framework that shifts from traditional macro placement to macro regulation by refining pre-existing chip layouts.
• The methodology integrates industry-standard regularity with HPWL optimization, addressing long training times and generalization issues.
• Experiments using Cadence Innovus on benchmarks like ISPD 2005 demonstrate significant improvements in routed wirelength, congestion, and overall PPA metrics.

Overview of "Reinforcement Learning Policy as Macro Regulator Rather than Macro Placer"

The paper "Reinforcement Learning Policy as Macro Regulator Rather than Macro Placer" proposes a novel application of reinforcement learning (RL) in the field of modern chip design, emphasizing the role of RL as a macro regulator rather than a placer. This paper offers an innovative solution to common challenges in chip placement, such as long training times, insufficient generalization, and unreliable improvements in power, performance, and area (PPA) metrics. The authors introduce a framework called MaskRegulate, focusing on refining existing placement layouts instead of generating placements from scratch, thereby enhancing the retrieval of informative states and precise rewards.

Key Contributions

1. Novel Problem Formulation: This paper shifts the RL approach from placing macros from scratch to adjusting pre-existing placements. This change capitalizes on the comprehensive information available in pre-existing placements, improving the precision and applicability of the RL strategies.
2. Integration of Regularity: The paper introduces the concept of "regularity" in the RL training process. Historically neglected, regularity aligns closely with industry standards, impacting manufacturability and ensuring optimal performance. By factoring regularity into reward signals, the proposed model guides placements towards configurations that improve both regularity and HPWL (half-perimeter wirelength).
3. Enhanced PPA Metrics: Experiments conducted on benchmarks such as ISPD 2005 and ICCAD 2015 demonstrate that the MaskRegulate method significantly enhances global HPWL, regularity, and multiple PPA metrics compared to existing approaches, affirming its practical efficiency and effectiveness in optimizing chip design.

Technical Details

• The approach is evaluated using the commercial Cadence Innovus tool, revealing substantial improvements in routed wirelength and congestion metrics.
• MaskRegulate demonstrates the ability to fine-tune placements from other methods, confirming its adaptability and potential as a universal tool for optimizing place-and-route processes in chip design.
• A notable emphasis is placed on integrating regularity into the RL framework, using a novel regularity mask, which accounts for distance from the chip's edges, thus helping to avoid macro blockages — a common pitfall in conventional placement schemas focusing solely on HPWL.

Implications and Future Directions

This work extends the scope of RL applications within the EDA (Electronic Design Automation) landscape, suggesting practical pathways for leveraging RL policies not only for placement but also for increasing optimization stages within chip design. Future work could explore more advanced RL architectures, such as transformers, to enhance the generalization of the RL regulator across diverse chip layouts. Additionally, incorporating global wirelength and timing considerations during training could increase alignment with real-world chip performance metrics.

In conclusion, by addressing inherent challenges in placement problem formulations and integrating industry-standard metrics like regularity, this paper sets a solid foundation for the continued exploration of RL in chip design. The MaskRegulate framework reveals the transformative potential of RL policies in refining chip placement, paving the way for more versatile and efficient design methodologies in the semiconductor industry.