In this paper, we present DevFormer, a novel transformer-based architecture for addressing the complex and computationally demanding problem of hardware design optimization. Despite the demonstrated efficacy of transformers in domains including natural language processing and computer vision, their use in hardware design has been limited by the scarcity of offline data. Our approach addresses this limitation by introducing strong inductive biases such as relative positional embeddings and action-permutation symmetricity that effectively capture the hardware context and enable efficient design optimization with limited offline data. We apply DevFormer to the problem of decoupling capacitor placement and show that it outperforms state-of-the-art methods in both simulated and real hardware, leading to improved performances while reducing the number of components by more than 30%. Finally, we show that our approach achieves promising results in other offline contextual learning-based combinatorial optimization tasks.

翻译：在本文中，我们提出DevFormer——一种基于Transformer的新型架构，用于解决硬件设计优化中复杂且计算密集的问题。尽管Transformer在自然语言处理和计算机视觉等领域已展现出卓越效能，但在硬件设计中的应用因离线数据的稀缺而受到限制。我们的方法通过引入强归纳偏置（如相对位置嵌入和动作排列对称性）来克服这一局限，这些偏置能有效捕捉硬件上下文，并利用有限的离线数据实现高效的设计优化。我们将DevFormer应用于去耦电容器布局问题，结果表明，在仿真和实际硬件中均优于现有最优方法，不仅提升了性能，还将器件数量减少了30%以上。最后，我们展示了该方法在其他基于离线上下文学习的组合优化任务中取得了具有前景的结果。