arXiv Machine Learning October 19, 2026 By Bernhard Klein, Falk Selker, Hendrik Borras, Sophie Steger, Franz Pernkopf, Holger Fr\"oning

Accelerated Execution of Bayesian Neural Networks using a Single Probabilistic Forward Pass and Code Generation

Read the original on arXiv Machine Learning →

The paper presents a pipeline for training, compiling, and deploying Bayesian neural networks (BNNs) that use a single probabilistic forward pass (PFP) instead of multiple stochastic samples. By assuming Gaussian-distributed weights and activations, PFP enables fully analytic uncertainty propagation, allowing a single deterministic forward pass to replace sampling. Using the TVM compiler and a library of Gaussian-propagating operators, the authors achieve up to 4200× speedups on small mini-batches while maintaining accuracy, uncertainty estimation, and out-of-domain detection comparable to traditional stochastic variational inference (SVI).

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