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 SPICE simulations, making effective design automation a challenging endeavor. In this paper, we introduce INSIGHT, a GPU-powered, technology-agnostic, effective universal neural simulator in the analog front-end design automation loop. INSIGHT accurately predicts the performance metrics of analog circuits across various technologies with just a few microseconds of 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 sizing framework with INSIGHT as the accurate surrogate, only requires < 20 real-time simulations with 100-1000x lower simulation costs and significant speedup over existing sizing methods.

翻译：模拟前端设计严重依赖专业的人工经验和成本高昂的试错仿真，这推动了先前许多关于模拟设计自动化的研究工作。然而，对广阔而复杂的设计空间进行高效探索，仍然受到SPICE仿真耗时特性的制约，使得有效的设计自动化成为一项具有挑战性的任务。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能够以仅需几微秒的推理时间，准确预测跨多种工艺的模拟电路性能指标。值得注意的是，其自回归能力使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特性。这种低成本和高保真度的特性，使INSIGHT成为模拟前端优化框架中标准仿真器的良好替代品。INSIGHT兼容任何优化框架，通过复杂的离线学习与适应技术，促进增强的设计空间探索以提高样本效率。我们的实验表明，以INSIGHT作为精确代理模型的、基于模型的批量强化学习电路尺寸优化框架INSIGHT-M，仅需少于20次实时仿真，其仿真成本比现有尺寸优化方法低100-1000倍，并实现了显著的加速。