4 citations · 5 across the 14 of their papers we have counts for
11 papers · 1 filter
Beyond Noise: Understanding and Overcoming Temperature Effects in Analog DNN Inference
Niklas Summ, Xiao Wang, Hendrik Borras +2
The energy efficiency of analog computing makes it one of the most promising candidates for deploying resource-intensive machine learning workloads on constrained platforms such as…
Constrained Co-Design for Photonic Bayesian Neural Networks
Hendrik Borras, Xiao Wang, Bernhard Klein +4
Classical neural networks frequently produce overconfident predictions on ambiguous or out-of-distribution (OOD) data, a liability that grows with each AI system deployed in safety…
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…
Uncertainty Reasoning with Photonic Bayesian Machines
F. Brückerhoff-Plückelmann, H. Borras, S. U. Hulyal +12
Artificial intelligence (AI) systems increasingly influence safety-critical aspects of society, from medical diagnosis to autonomous mobility, making uncertainty awareness a centra…
Accelerated Execution of Bayesian Neural Networks using a Single Probabilistic Forward Pass and Code Generation
Bernhard Klein, Falk Selker, Hendrik Borras +3
Machine learning models perform well across domains such as diagnostics, weather forecasting, NLP, and autonomous driving, but their limited uncertainty handling restricts use in s…
Resource-Efficient and Robust Inference of Deep and Bayesian Neural Networks on Embedded and Analog Computing Platforms
Bernhard Klein
While modern machine learning has transformed numerous application domains, its growing computational demands increasingly constrain scalability and efficiency, particularly on emb…
Variance-Aware Noisy Training: Hardening DNNs against Unstable Analog Computations
Xiao Wang, Hendrik Borras, Bernhard Klein +1
The disparity between the computational demands of deep learning and the capabilities of compute hardware is expanding drastically. Although deep learning achieves remarkable perfo…
On Hardening DNNs against Noisy Computations
Xiao Wang, Hendrik Borras, Bernhard Klein +1
The success of deep learning has sparked significant interest in designing computer hardware optimized for the high computational demands of neural network inference. As further mi…
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…
Towards Hardware-Specific Automatic Compression of Neural Networks
Torben Krieger, Bernhard Klein, Holger Fröning
Compressing neural network architectures is important to allow the deployment of models to embedded or mobile devices, and pruning and quantization are the major approaches to comp…
Walking Noise: On Layer-Specific Robustness of Neural Architectures against Noisy Computations and Associated Characteristic Learning Dynamics
Hendrik Borras, Bernhard Klein, Holger Fröning
Deep neural networks are extremely successful in various applications, however they exhibit high computational demands and energy consumption. This is exacerbated by stuttering tec…