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The Computing Systems Group has partnered with colleagues from the Institute for Micro-Electronics (IMECAS), Chinese Academy of Sciences, Xi’an University of Technology (XAUT), and the Engineering Mathematics and Computing Lab (EMCL) of Heidelberg University to pursue research on system-level aspects of resistive RAM as emerging memory technology. In this context, a deep collaboration with the Chinese colleagues including mutual research visits and workshops is envisioned. Research will gear to explore the various trade-offs of this memory technology from an application perspective, including scientific-technical computations as well as emerging machine learning tasks, with trade-offs including performance, power consumption, reliability and durability. The project called "Khunjerab: Bridging Applications and Future Emerging Memory for Different Performance-Power-Reliability Trade-Offs” has just received funding by the National Natural Science Foundation of China (NSFC) and DFG (Germany).

Die interdisziplinäre Praxisstudie der Universität Heidelberg wird von der Carl-Zeiss-Stiftung mit rund 4,5 Millionen Euro gefördert. Prof. Dr. Lorenzo Masia ist einer der Projektleiter der Studie.

Weitere Informationen finden Sie in der Pressemitteilung vom 28.01.2021.

 

 

Am Institut für Technische Informatik sind aktuell zwei Professuren ausgeschrieben:

The Computing Systems Group’s co-organized WEML workshop as been covered in the most recent HiPEAC magazine: https://www.hipeac.net/magazine/7154/

Jointly with colleagues from Graz University of Technology, University of Duisburg-Essen, and XILINX Research, the Computing Systems Group will organize the ITEM Workshop (IoT, Edge, and Mobile for Embedded Machine Learning, https://www.item-workshop.org), collocated with ECML-PKDD2020. The goal of this workshop is to further contribute to bridging the machine learning and embedded systems domain, exploring and discussing the use of methods such as neural network compression, continuous learning, automated code generation for various embedded targets, tool-driven optimizations of mappings, future emerging technologies, new applications, and security and privacy of embedded machine learning.

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