Scaling Up Thermodynamic AI Models
Thermodynamic computing devices based on the Ising model show great promise for low-power AI inference and edge computing, but scalable training methods remain limited. This paper presents a scalable backpropagation-based algorithm for training deep convolutional networks on Ising machines, achieving 94.9% on CIFAR-10 and 76.0% on CIFAR-100. It also develops a theory for the tradeoff between inference cost and accuracy, and discusses implications for hardware development.
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[Submitted on 30 Jun 2026]
Title:Scaling Up Thermodynamic AI Models
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Abstract:Thermodynamic computing devices based on the Ising model show great promise for low-power AI inference and edge computing, but scalable methods for training large models for such hardware remain limited. Prior theory shows that the time-averaged behavior of high-temperature Gibbs-sampled Ising systems can implement feed-forward neural inference. We turn this theoretical correspondence into a scalable and purely backpropagation-based algorithm for training deep convolutional networks for thermodynamic inference on Ising machine hardware. Our image classification models achieve accuracies of 94.9% on CIFAR-10 and 76.0% on CIFAR-100 under binary Gibbs sampling. We then develop and experimentally validate a mathematical theory relating inference cost to accuracy and controlling autocorrelation times. Subsequently, we calculate asymptotic results showing that inference cost is bounded by a well-controlled tradeoff with performance and exhibit algorithms for computing optimal inference schedules. Finally, we discuss implications for hardware development and the future of high-temperature thermodynamic AI models.
Subjects:
Machine Learning (cs.LG); Disordered Systems and Neural Networks (cond-mat.dis-nn); Artificial Intelligence (cs.AI)
Cite as: arXiv:2607.00170 [cs.LG]
(or arXiv:2607.00170v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2607.00170
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Andrew Moore [view email] [v1] Tue, 30 Jun 2026 20:44:57 UTC (3,936 KB)
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