Kongress und Messe  /  09. März 2020  -  13. März 2020

DATE 2020 - Design, Automation and Test in Europe Conference

Die »DATE 2020 - Design, Automation and Test in Europe Conference 2020« wird aufgrund der aktuellen Entwicklungen hinsichtlich des Corona-Virus in diesem Jahr virtuell stattfinden. Das Datum hierzu wird noch festgelegt.

Weitere Informationen finden Sie hier.


Die Veranstaltung »DATE 2020« gehört zu den wichtigsten europäischen Veranstaltungen im Bereich Eingebetteter Systeme und besteht aus einer Konferenz mit eingeladenen Vorträgen, Themensitzungen, Tutorien und Workshops.

Auch das Fraunhofer IESE nimmt mit einem Vortag teil:


Mittwoch, 10.03.2020, Dr. Matthias Jung

Virtual platforms are state-of-the-art for design space exploration and simulation of today's complex Systems on Chips (SoCs). The challenge of these virtual platforms is to find the right trade-off between speed and accuracy. For the simulation of Dynamic Random Access Memories (DRAMs), which have complex timing and power behavior, high-accuracy models are needed. However, cycle-accurate DRAM models take up a huge portion of the overall simulation time. Therefore, it is important to accelerate DRAM simulation models while maintaining accuracy. In the literature, different approaches to accelerating the speed of DRAM simulations on virtual platforms already do exist. This paper proposes two new performance-optimized DRAM models that further accelerate the speed of simulations with merely a negligible degradation in accuracy. The first model is an enhanced Transaction Level Model (TLM), which uses a look-up table to accelerate simulation parts with high bandwidth usage for online scenarios. The other one is a neural-network-based simulator for offline trace analysis. We will show a mathematical methodology for generating the inputs for the look-up table and the optimal artificial training set for the neural network. The TLM model is up to ~5 times faster than a state-of-the-art TLM DRAM simulator. The neural network is able to speed up to ~10x while inferring on a GPU. Both solutions provide only a slight decrease in accuracy, approximately 5%.