5 papers
Probabilistic Photonic Computing
Frank Brückerhoff-Plückelmann, Anna P. Ovvyan, Akhil Varri +8
Probabilistic computing excels in approximating combinatorial problems and modelling uncertainty. However, using conventional deterministic hardware for probabilistic models is cha…
Uncertainty-Preserving QBNNs: Multi-Level Quantization of SVI-Based Bayesian Neural Networks for Image Classification
Hendrik Borras, Yong Wu, Bernhard Klein +1
Bayesian Neural Networks (BNNs) provide principled uncertainty quantification but suffer from substantial computational and memory overhead compared to deterministic networks. Whil…
Function Space Diversity for Uncertainty Prediction via Repulsive Last-Layer Ensembles
Sophie Steger, Christian Knoll, Bernhard Klein +2
Bayesian inference in function space has gained attention due to its robustness against overparameterization in neural networks. However, approximating the infinite-dimensional fun…
Less Memory Means smaller GPUs: Backpropagation with Compressed Activations
Daniel Barley, Holger Fröning
The ever-growing scale of deep neural networks (DNNs) has lead to an equally rapid growth in computational resource requirements. Many recent architectures, most prominently Large…
Understanding Cache Boundness of ML Operators on ARM Processors
Bernhard Klein, Christoph Gratl, Manfred Mücke +1
Machine Learning compilers like TVM allow a fast and flexible deployment on embedded CPUs. This enables the use of non-standard operators, which are common in ML compression techni…