6 citations · 11 across the 9 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…
Resource-Efficient Speech Mask Estimation for Multi-Channel Speech Enhancement
Lukas Pfeifenberger, Matthias Zöhrer, Günther Schindler +3
While machine learning techniques are traditionally resource intensive, we are currently witnessing an increased interest in hardware and energy efficient approaches. This need for…
Non-Relational Databases on FPGAs: Survey, Design Decisions, Challenges
Jonas Dann, Daniel Ritter, Holger Fröning
Non-relational database systems (NRDS), such as graph, document, key-value, and wide-column, have gained much attention in various trending (business) application domains like smar…
On the Difficulty of Designing Processor Arrays for Deep Neural Networks
Kevin Stehle, Günther Schindler, Holger Fröning
Systolic arrays are a promising computing concept which is in particular inline with CMOS technology trends and linear algebra operations found in the processing of artificial neur…
A Simple Model for Portable and Fast Prediction of Execution Time and Power Consumption of GPU Kernels
Lorenz Braun, Sotirios Nikas, Chen Song +2
Characterizing compute kernel execution behavior on GPUs for efficient task scheduling is a non-trivial task. We address this with a simple model enabling portable and fast predict…
Parameterized Structured Pruning for Deep Neural Networks
Guenther Schindler, Wolfgang Roth, Franz Pernkopf +1
As a result of the growing size of Deep Neural Networks (DNNs), the gap to hardware capabilities in terms of memory and compute increases. To effectively compress DNNs, quantizatio…
Efficient and Robust Machine Learning for Real-World Systems
Franz Pernkopf, Wolfgang Roth, Matthias Zoehrer +7
While machine learning is traditionally a resource intensive task, embedded systems, autonomous navigation and the vision of the Internet-of-Things fuel the interest in resource ef…