Presentation
INSIGHT: A Universal Neural Simulator Framework for Analog Circuits with Autoregressive Transformers
DescriptionThis paper introduces INSIGHT, a data-efficient, adaptive, high-fidelity, technology-agnostic universal neural simulator framework that formulates analog performance prediction as an autoregressive sequence generation task to accurately predict performance across diverse circuits. INSIGHT achieves test R2 scores ≥0.95, outperforming existing neural surrogates. Cross-technology transfer learning experiments show that INSIGHT can preserve model performance with ~60% less training data. Low-Rank Adaptation (LoRA) integration further reduces memory footprint by ~42% and training time by ~25%, maintaining high performance. Our experiments show that INSIGHT-based RL sizing framework achieves ~100-1000X lower simulation costs over existing sizing methods for identical benchmarks and target specifications.
Event Type
Research Manuscript
TimeMonday, June 2311:00am - 11:15am PDT
Location3006, Level 3
EDA6: Analog CAD, Simulation, Verification and Test
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