Analog front-end design heavily relies on specialized human expertise and costly trial-and-error simulations, which motivated many prior works on analog design automation. However, efficient and effective exploration of the vast and complex design space remains constrained by the time-consuming nature of CPU-based SPICE simulations, making effective design automation a challenging endeavor. In this paper, we introduce INSIGHT, a GPU-powered, technology-independent, effective universal neural simulator in the analog front-end design automation loop. INSIGHT accurately predicts the performance metrics of analog circuits across various technology nodes, significantly reducing inference time. Notably, its autoregressive capabilities enable INSIGHT to accurately predict simulation-costly critical transient specifications leveraging less expensive performance metric information. The low cost and high fidelity feature make INSIGHT a good substitute for standard simulators in analog front-end optimization frameworks. INSIGHT is compatible with any optimization framework, facilitating enhanced design space exploration for sample efficiency through sophisticated offline learning and adaptation techniques. Our experiments demonstrate that INSIGHT-M, a model-based batch reinforcement learning framework that leverages INSIGHT for analog sizing, achieves at least 50X improvement in sample efficiency across circuits. To the best of our knowledge, this marks the first use of autoregressive transformers in analog front-end design.

翻译：模拟前端设计严重依赖专业的人工经验与成本高昂的试错式仿真，这推动了先前许多关于模拟设计自动化的研究。然而，基于CPU的SPICE仿真耗时严重，制约了对广阔而复杂设计空间的高效探索，使得有效的设计自动化仍具挑战性。本文提出INSIGHT，一种在模拟前端设计自动化流程中使用的、基于GPU的、与工艺无关的高效通用神经仿真器。INSIGHT能够准确预测跨不同工艺节点的模拟电路性能指标，并显著减少推理时间。值得注意的是，其自回归能力使得INSIGHT能够利用成本较低的静态性能指标信息，准确预测仿真成本高昂的关键瞬态规格。低成本与高保真特性使INSIGHT成为模拟前端优化框架中标准仿真器的理想替代品。INSIGHT兼容任何优化框架，通过复杂的离线学习与自适应技术，促进增强的设计空间探索以提高样本效率。实验表明，基于模型的批量强化学习框架INSIGHT-M（利用INSIGHT进行模拟电路尺寸设计）在多种电路上实现了至少50倍的样本效率提升。据我们所知，这是自回归Transformer在模拟前端设计中的首次应用。