mlsanityData to 7 Oct 2026

INSIGHT: Universal Neural Simulator for Analog Circuits Harnessing Autoregressive Transformers

arXiv 2407.07346 ↗First posted 10 Jul 2024cs.LG cs.CEcited by 8

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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.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 22 of its 49 references that are in the map. First posted on arXiv 10 Jul 2024.

Builds on

22 of 49 references.

References · 22

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10 more
  1. 2020GCN-RL Circuit Designer: Transferable Transistor Sizing with Graph Neural Networks and Reinforcement Learningcited by 89
  2. 2024AnalogCoder: Analog Circuit Design via Training-Free Code Generationcited by 82
  3. 2020AutoCkt: Deep Reinforcement Learning of Analog Circuit Designscited by 51
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  7. 2021DNN-Opt: An RL Inspired Optimization for Analog Circuit Sizing using Deep Neural Networkscited by 18
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Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 3; 2025: 4; 2026: 1.

8 papers.

  1. 2024ControlAgent: Automating Control System Design via Novel Integration of LLM Agents and Domain Expertisecited by 20
  2. 2025AnalogCoder-Pro: Unifying Analog Circuit Generation and Optimization via Multi-modal LLMscited by 14
  3. 2025DICE: Device-level Integrated Circuits Encoder with Graph Contrastive Pretrainingcited by 2
  4. 2025ZeroSim: Zero-Shot Analog Circuit Evaluation with Unified Transformer Embeddingscited by 2
  5. 2024CktGen: Automated Analog Circuit Design with Generative Artificial Intelligencecited by 1
  6. 2024M3: Mamba-assisted Multi-Circuit Optimization via MBRL with Effective Schedulingcited by 0
  7. 2025AnalogFed: Privacy-Preserving Discovery of Analog Circuits at Scale with Federated Generative AIcited by 0
  8. 2026ARCS: Autoregressive Circuit Synthesis with Topology-Aware Graph Attention and Spec Conditioningcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025AUTOCIRCUIT-RL: Reinforcement Learning-Driven LLM for Automated Circuit Topology Generation
  2. 2025AnalogSAGE: Self-evolving Analog Design Multi-Agents with Stratified Memory and Grounded Experience
  3. 2026AutoSizer: Automatic Sizing of Analog and Mixed-Signal Circuits via Large Language Model (LLM) Agents
  4. 2021A flexible and fast PyTorch toolkit for simulating training and inference on analog crossbar arrays
  5. 2025White-Box Reasoning: Synergizing LLM Strategy and gm/Id Data for Automated Analog Circuit Design
  6. 2026EXPLORE: Exploration with Guided Search for Analog Topology Generation using Language Models
  7. 2026Simulation-Aware In-Context Policy Improvement for LLM-Aided Analog Layout Refinement
  8. 2020Analog Circuit Design with Dyna-Style Reinforcement Learning