2 citations · 4 across the 5 of their papers we have counts for
12 papers
HW-Aware Initialization of DNN Auto-Tuning to Improve Exploration Time and Robustness
Dennis Rieber, Moritz Reiber, Oliver Bringmann +1
The process of optimizing the latency of DNN operators with ML models and hardware-in-the-loop, called auto-tuning, has established itself as a pervasive method for the deployment…
Scheduling of Graph Queries: Controlling Intra- and Inter-query Parallelism for a High System Throughput
Matthias Hauck, Ismail Oukid, Holger Fröning
The vast amounts of data used in social, business or traffic networks, biology and other natural sciences are often managed in graph-based data sets, consisting of a few thousand u…
Joint Program and Layout Transformations to enable Convolutional Operators on Specialized Hardware based on Constraint Programming
Dennis Rieber, Axel Acosta, Holger Fröning
The success of Deep Artificial Neural Networks (DNNs) in many domains created a rich body of research concerned with hardware accelerators for compute-intensive DNN operators. Howe…
Demystifying Memory Access Patterns of FPGA-Based Graph Processing Accelerators
Jonas Dann, Daniel Ritter, Holger Fröning
Recent advances in reprogrammable hardware (e.g., FPGAs) and memory technology (e.g., DDR4, HBM) promise to solve performance problems inherent to graph processing like irregular m…
Exploring Memory Access Patterns for Graph Processing Accelerators
Jonas Dann, Daniel Ritter, Holger Fröning
Recent trends in business and technology (e.g., machine learning, social network analysis) benefit from storing and processing growing amounts of graph-structured data in databases…
On Resource-Efficient Bayesian Network Classifiers and Deep Neural Networks
Wolfgang Roth, Günther Schindler, Holger Fröning +1
We present two methods to reduce the complexity of Bayesian network (BN) classifiers. First, we introduce quantization-aware training using the straight-through gradient estimator…