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arXiv · 2610.21591

LOADBench: A Large Open Analog Dataset and Benchmark for Machine Learning in IC Design

M. Leibl·F. Azevedo·S. Uhlich·A. Bonetti·A. Venkitaraman·C. Hsieh·P. Pham·L. Servadei·H. Graeb·R. Martins
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Abstract

Machine learning (ML) for analog IC design is constrained by limited open data and the absence of standardized evaluation: results reported in different papers are typically obtained on bespoke, often closed, datasets and with ad-hoc metrics, leaving comparisons across methods unreliable. We address both gaps with a large opensource dataset and a suite of standardized benchmarks. The dataset contains more than 100 operational amplifier (opamp) topologies (1-, 2-, and 3-stage) in the SkyWater130 PDK, each shipped with structural and functional annotations, topology-specific sizing constraints (symmetry and sizing rules), an Ngspice testbench, and a multi-objective NSGA-II sizing run producing around 103 Pareto-optimal and 106 intermediate sizing-performance pairs per topology. On top of this data we define six benchmark tasks with precise protocols: inverse sizing, topology selection, sub-circuit classification, structural circuit generation, Pareto-membership prediction, and multi-objective optimization. Thus, the data presented here constitutes the most comprehensive dataset for ML development in over four decades of analog IC design automation. The dataset, ground-truth annotations, and reference implementations of the metric-defining routines are released under an open license, enabling reproducible and directly comparable evaluation across heterogeneous analog design automation methods.

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M. Leibl, F. Azevedo, S. Uhlich, A. Bonetti, A. Venkitaraman, C. Hsieh, P. Pham, L. Servadei, H. Graeb, R. Martins. 2026-10-21. LOADBench: A Large Open Analog Dataset and Benchmark for Machine Learning in IC Design. https://doi.org/10.1145/3831599.3840321

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