1 code implementation • 22 Oct 2020 • Wolfgang Roth, Günther Schindler, Holger Fröning, Franz Pernkopf
We present two methods to reduce the complexity of Bayesian network (BN) classifiers.
no code implementations • 22 Jul 2020 • Lukas Pfeifenberger, Matthias Zöhrer, Günther Schindler, Wolfgang Roth, Holger Fröning, Franz Pernkopf
While machine learning techniques are traditionally resource intensive, we are currently witnessing an increased interest in hardware and energy efficient approaches.
1 code implementation • 24 Jun 2020 • Kevin Stehle, Günther Schindler, Holger Fröning
We present an analysis of popular DNN models to illustrate how it can estimate required cycles, data movement costs, as well as systolic array utilization, and show how the progress in network architecture design impacts the efficiency of inference on accelerators based on systolic arrays.
no code implementations • 7 Jan 2020 • Wolfgang Roth, Günther Schindler, Bernhard Klein, Robert Peharz, Sebastian Tschiatschek, Holger Fröning, Franz Pernkopf, Zoubin Ghahramani
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-efficient approaches.
no code implementations • ICLR 2019 • Günther Schindler, Wolfgang Roth, Franz Pernkopf, Holger Fröning
In this work we propose a method for weight and activation quantization that is scalable in terms of quantization levels (n-ary representations) and easy to compute while maintaining the performance close to full-precision CNNs.