Repository for the paper "Towards Hardware-Aware Tractable Learning of Probabilistic Models", to be presented in NeurIPS 2019.

• Python 2.7 (code soon to be updated for Python 3)

The goal is to find the Pareto optimal set of configurations in the accuracy vs hardware-cost space by scaling tunable system properties. The properties to consider can be given as options as follows:

• -ms: Scale model complexity
• -csi: Scale sensor interfaces (prune features, sensors and simplify model)
• -ps: Scale precision

For the banknote benchmark, following the full scaling pipeline (model complexity scaling - sensor interfaces scale - precision scale), starting from models 11,22 and 38:

python hwopt.py banknote -models 10,22,38  -ms -ps -csi

We have included the ACs used in our experiments, trained using the LearnPsdd algorithm introduced in ¹

For reproducibility, we have included the binarized and randomly split classification datasets used for the experiments: banknote², HAR³, HAR_multiclass³ ,houses⁴ ,madelone ⁵ and wilt⁶. Density estimation datasets NLTCS and Jester were taken from https://github.com/UCLA-StarAI/Density-Estimation-Datasets, and introduced in⁷.

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