# INSIGHT: Universal Neural Simulator for Analog Circuits Harnessing Autoregressive Transformers

arXiv: https://arxiv.org/abs/2407.07346 · HTML: https://mlsanity.com/paper/2407.07346/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 49 references that are in the map.
- First posted on arXiv 10 Jul 2024.

## Builds on (most cited first, 22 of 22)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [SGDR: Stochastic Gradient Descent with Warm Restarts](https://mlsanity.com/paper/1608.03983/) (2016)
- [Gaussian Error Linear Units (GELUs)](https://mlsanity.com/paper/1606.08415/) (2016)
- [A Comprehensive Survey on Graph Neural Networks](https://mlsanity.com/paper/1901.00596/) (2019)
- [Decision Transformer: Reinforcement Learning via Sequence Modeling](https://mlsanity.com/paper/2106.01345/) (2021)
- [Fundamentals of Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) Network](https://mlsanity.com/paper/1808.03314/) (2018)
- [TSMixer: An All-MLP Architecture for Time Series Forecasting](https://mlsanity.com/paper/2303.06053/) (2023)
- [Online Decision Transformer](https://mlsanity.com/paper/2202.05607/) (2022)
- [Graph Inductive Biases in Transformers without Message Passing](https://mlsanity.com/paper/2305.17589/) (2023)
- [GCN-RL Circuit Designer: Transferable Transistor Sizing with Graph Neural Networks and Reinforcement Learning](https://mlsanity.com/paper/2005.00406/) (2020)
- [AnalogCoder: Analog Circuit Design via Training-Free Code Generation](https://mlsanity.com/paper/2405.14918/) (2024)
- [AutoCkt: Deep Reinforcement Learning of Analog Circuit Designs](https://mlsanity.com/paper/2001.01808/) (2020)
- [MaskPlace: Fast Chip Placement via Reinforced Visual Representation Learning](https://mlsanity.com/paper/2211.13382/) (2022)
- [ChiPFormer: Transferable Chip Placement via Offline Decision Transformer](https://mlsanity.com/paper/2306.14744/) (2023)
- [BANG: Bridging Autoregressive and Non-autoregressive Generation with Large Scale Pretraining](https://mlsanity.com/paper/2012.15525/) (2020)
- [DNN-Opt: An RL Inspired Optimization for Analog Circuit Sizing using Deep Neural Networks](https://mlsanity.com/paper/2110.00211/) (2021)
- [Domain Knowledge-Infused Deep Learning for Automated Analog/Radio-Frequency Circuit Parameter Optimization](https://mlsanity.com/paper/2204.12948/) (2022)
- [Scalable and Effective Arithmetic Tree Generation for Adder and Multiplier Designs](https://mlsanity.com/paper/2405.06758/) (2024)
- [Genetic algorithms with DNN-based trainable crossover as an example of partial specialization of general search](https://mlsanity.com/paper/1809.04520/) (2018)

## Built on by (most cited first, 8 of 8)

- [ControlAgent: Automating Control System Design via Novel Integration of LLM Agents and Domain Expertise](https://mlsanity.com/paper/2410.19811/) (2024)
- [AnalogCoder-Pro: Unifying Analog Circuit Generation and Optimization via Multi-modal LLMs](https://mlsanity.com/paper/2508.02518/) (2025)
- [CktGen: Automated Analog Circuit Design with Generative Artificial Intelligence](https://mlsanity.com/paper/2410.00995/) (2024)
- [DICE: Device-level Integrated Circuits Encoder with Graph Contrastive Pretraining](https://mlsanity.com/paper/2502.08949/) (2025)
- [ZeroSim: Zero-Shot Analog Circuit Evaluation with Unified Transformer Embeddings](https://mlsanity.com/paper/2511.07658/) (2025)
- [ARCS: Autoregressive Circuit Synthesis with Topology-Aware Graph Attention and Spec Conditioning](https://mlsanity.com/paper/2603.29068/) (2026)
- [M3: Mamba-assisted Multi-Circuit Optimization via MBRL with Effective Scheduling](https://mlsanity.com/paper/2411.16019/) (2024)
- [AnalogFed: Privacy-Preserving Discovery of Analog Circuits at Scale with Federated Generative AI](https://mlsanity.com/paper/2507.15104/) (2025)

## Abstract (arXiv)

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.