14 citations · 34 across the 8 of their papers we have counts for
9 papers
vAccSOL: Efficient and Transparent AI Vision Offloading for Mobile Robots
Adam Zahir, Michele Gucciardom Falk Selker, Anastasios Nanos +4
Mobile robots are increasingly deployed for inspection, patrol, and search-and-rescue operations, relying on computer vision for perception, navigation, and autonomous decision-mak…
Higher-Rank Irreducible Cartesian Tensors for Equivariant Message Passing
Viktor Zaverkin, Francesco Alesiani, Takashi Maruyama +5
The ability to perform fast and accurate atomistic simulations is crucial for advancing the chemical sciences. By learning from high-quality data, machine-learned interatomic poten…
SOL: Reducing the Maintenance Overhead for Integrating Hardware Support into AI Frameworks
Nicolas Weber
The increased interest in Artificial Intelligence (AI) raised the need for highly optimized and sophisticated AI frameworks. Starting with the Lua-based Torch many frameworks have…
A Needle in a Haystack -- How to Derive Relevant Scenarios for Testing Automated Driving Systems in Urban Areas
Nico Weber, Christoph Thiem, Ulrich Konigorski
While there was great progress regarding the technology and its implementation for vehicles equipped with automated driving systems (ADS), the problem of how to proof their safety…
Toward Generating Sufficiently Valid Test Case Results: A Method for Systematically Assigning Test Cases to Test Bench Configurations in a Scenario-Based Test Approach for Automated Vehicles
Markus Steimle, Nico Weber, Markus Maurer
To successfully launch automated vehicles into the consumer market, there must be credible proof that the vehicles will operate safely. However, finding a method to validate the ve…
HALF: Holistic Auto Machine Learning for FPGAs
Jonas Ney, Dominik Loroch, Vladimir Rybalkin +3
Deep Neural Networks (DNNs) are capable of solving complex problems in domains related to embedded systems, such as image and natural language processing. To efficiently implement…
SOL: Effortless Device Support for AI Frameworks without Source Code Changes
Nicolas Weber, Felipe Huici
Modern high performance computing clusters heavily rely on accelerators to overcome the limited compute power of CPUs. These supercomputers run various applications from different…
BrainSlug: Transparent Acceleration of Deep Learning Through Depth-First Parallelism
Nicolas Weber, Florian Schmidt, Mathias Niepert +1
Neural network frameworks such as PyTorch and TensorFlow are the workhorses of numerous machine learning applications ranging from object recognition to machine translation. While…
Detail-Preserving Pooling in Deep Networks
Faraz Saeedan, Nicolas Weber, Michael Goesele +1
Most convolutional neural networks use some method for gradually downscaling the size of the hidden layers. This is commonly referred to as pooling, and is applied to reduce the nu…