Chip Placement with Diffusion

• URL: http://arxiv.org/abs/2407.12282v1
• Date: Wed, 17 Jul 2024 03:02:24 GMT
• Title: Chip Placement with Diffusion
• Authors: Vint Lee, Chun Deng, Leena Elzeiny, Pieter Abbeel, John Wawrzynek,
• Abstract summary: Macro placement defines the physical location of large collections of components, known as macros, on a 2-dimensional chip. Existing learning-based methods fall short because of their reliance on reinforcement learning, which is slow and limits the flexibility of the agent. We propose a novel architecture for the denoising model, as well as an algorithm to generate large synthetic datasets for pre-training. We empirically show that our model can tackle the placement task, and achieve competitive performance on placement benchmarks compared to state-of-the-art methods.
• Score: 42.397340832801724
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Macro placement is a vital step in digital circuit design that defines the physical location of large collections of components, known as macros, on a 2-dimensional chip. The physical layout obtained during placement determines key performance metrics of the chip, such as power consumption, area, and performance. Existing learning-based methods typically fall short because of their reliance on reinforcement learning, which is slow and limits the flexibility of the agent by casting placement as a sequential process. Instead, we use a powerful diffusion model to place all components simultaneously. To enable such models to train at scale, we propose a novel architecture for the denoising model, as well as an algorithm to generate large synthetic datasets for pre-training. We empirically show that our model can tackle the placement task, and achieve competitive performance on placement benchmarks compared to state-of-the-art methods.

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