Towards scalable and robust sumproduct networks SumProduct Networks (SPNs) and their credal counterparts are machine learning models that combine good representational power with tractable inference. Yet they often have thousands of nodes which result in high processing times. We propose the addition of caches to the SPN nodes and show how this memoisation technique reduces inference times in a range of experiments. Moreover, we introduce classselective SPNs, an architecture that is suited for classification tasks and enables efficient robustness computation in Credal SPNs. We also illustrate how robustness estimates relate to reliability through the accuracy of the model, and how one can explore robustness in ensemble modelling.

ResourceEfficient Logarithmic Number Scale Arithmetic for SPN Inference on FPGAs FPGAs have been successfully used for the implementation of dedicated accelerators for a wide range of machine learning problems. The inference in socalled SumProduct Networks can also be accelerated efficiently using a pipelined FPGA architecture. However, as SumProduct Networks compute exact probability values, the required arithmetic precision poses different challenges than those encountered with Neural Networks. In previous work, this precision was maintained by using doubleprecision floatingpoint number formats, which are expensive to implement in FPGAs. In this work, we propose the use of a logarithmic number system format tailored specifically towards the inference in SumProduct Networks. The evaluation of our optimized arithmetic hardware operators shows that the use of logarithmic number formats allows to save up to 50% hardware resources compared to doubleprecision floating point, while maintaining sufficient precision for SPN inference at almost identical performance.