Chip Placement with Deep Reinforcement Learning (2004.10746v1)

Abstract: In this work, we present a learning-based approach to chip placement, one of the most complex and time-consuming stages of the chip design process. Unlike prior methods, our approach has the ability to learn from past experience and improve over time. In particular, as we train over a greater number of chip blocks, our method becomes better at rapidly generating optimized placements for previously unseen chip blocks. To achieve these results, we pose placement as a Reinforcement Learning (RL) problem and train an agent to place the nodes of a chip netlist onto a chip canvas. To enable our RL policy to generalize to unseen blocks, we ground representation learning in the supervised task of predicting placement quality. By designing a neural architecture that can accurately predict reward across a wide variety of netlists and their placements, we are able to generate rich feature embeddings of the input netlists. We then use this architecture as the encoder of our policy and value networks to enable transfer learning. Our objective is to minimize PPA (power, performance, and area), and we show that, in under 6 hours, our method can generate placements that are superhuman or comparable on modern accelerator netlists, whereas existing baselines require human experts in the loop and take several weeks.

• The paper introduces a deep reinforcement learning method that optimizes chip placement by minimizing power, performance, and area constraints.
• It employs a supervised pre-training phase combined with RL to enhance generalization across diverse chip designs and speed up convergence.
• Quantitative results show the technique produces high-quality placements in under six hours, outperforming traditional methods and expert designs.

Chip Placement with Deep Reinforcement Learning

The paper "Chip Placement with Deep Reinforcement Learning" introduces a novel approach to the chip placement process, framing it as a Reinforcement Learning (RL) problem to address the complexities inherent in optimizing power, performance, and area (PPA) in chip design. This work represents a significant stride in applying machine learning to one of the most intricate aspects of chip design, namely, the placement of electronic components on a chip canvas.

The approach involves training an RL agent to place the nodes of a chip netlist efficiently. The key innovation lies in the ability of this method to generalize across different chip blocks, thereby allowing the model to learn and improve as it is exposed to a larger variety of chip designs. Such generalization is facilitated by grounding the representation learning in a supervised task, aiming to predict placement quality via a neural network architecture that subsequently serves as the encoder for both policy and value networks. The primary goal is to minimize the PPA while managing constraints related to placement density and routing congestion.

A significant strength of the proposed method is its ability to produce optimized placements in under six hours, surpassing or equaling the performance of human experts who might take several weeks to achieve comparable results. This temporal efficiency is remarkable, given that current methods entail iterations over several weeks, requiring human expertise to meet the multi-faceted design criteria.

The paper thoroughly contrasts its methods with existing techniques across decades of chip placement research, including partitioning-based methods, stochastic/hill-climbing methods, and modern analytic techniques such as force-directed methods and electrostatics-based methods. The authors claim superiority in both the quality and time efficiency of placements compared to these traditional methods, especially highlighting the capacity for domain adaptation and transfer learning achieved by their approach.

Key methodological components of this research involve:

1. Defining the chip placement task within the framework of a Markov Decision Process (MDP), delineating states, actions, state transitions, and reward formulations.
2. Employing an innovative reward structure that combines proxy wirelength and congestion estimates, which are known to correlate with broader performance metrics.
3. Implementing a supervised pre-training phase that generates initial representations transferable to the RL policy network, significantly improving convergence speed and result quality.

Quantitative results indicate the method's ability to outperform state-of-the-art techniques such as RePlAce and manual design processes across various performance metrics, including timing, power, and wirelength. Moreover, visual inspections of placements revealed the RL approach's proficiency in achieving layouts that human designers typically strive for—standard cells optimally centered and macros efficiently placed around them.

The implications of this work extend beyond mere improvements in chip placement, suggesting that RL methodologies could be tailored to other stages of chip design, enabling more integrated and rapid hardware development cycles. Furthermore, as the RL model accumulates experience, its continued adaptation could streamline processes across an even wider array of design challenges, ultimately fostering synergies between AI advancements and hardware design efficiencies.

In conclusion, while the potential for further optimization remains, particularly in areas such as standard cell placement and macro ordering, this deep reinforcement learning method represents a noteworthy advancement in automating the chip design process. It paves the way for AI-driven enhancements in semiconductor manufacturing, aligning with the ever-increasing demands of AI compute paradigms.