Mark Sagi, M.Sc.

Wissenschaftlicher Mitarbeiter  

Technische Universität München
Fakultät für Elektrotechnik und Informationstechnik
Lehrstuhl für Integrierte Systeme
Arcisstr. 21
80290 München

Tel.: +
Fax: +
Gebäude: N1 (Theresienstr. 90)
Raum: N2117

Curriculum Vitae

Before joining the Chair for Integrated Systems in 2016, I worked as patent advisor at Leonhard & Collegen. My master's thesis was about "Joint Channel Coding in Heterogeneous Radio Environments" in cooperation with Intel in 2015. Furthermore, I worked as student at Rohde & Schwarz, Infineon and ESG (BMW) and tutored in "adveisor", math, programming in C and a practical course on electrical engineering. I also studied for a semester at ETH Zürich.

I speak fluently german, english and hungarian.

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Representation Learning for Multicore Power/Thermo Features

Representation Learning for Multicore Power/Thermo Features

Machine learning, representation learning, multicore, power, temperature



Reducing the power consumption of multicore processors is an ongoing and challenging task for processor designers. With increasing transistor count, power and thermal information is increasingly difficult to obtain. To obtain more and reliabe power/thermo information, designers are starting to use novel machine learning algorithms. In this seminar, you will investigate different representation learning algorithms – a subset of machine learning algorithms – for identifying power/thermo features on chip. You will make an overview over related work for multicore reprenstation learning and finally make an educated guess which representation learning algorithms are best suited for identiyfing power/thermo features.





My research focus is on power management for multi-core processors which are constrained by dark silicon.
Due to increasing power density, a decreasing ratio of transistors can be powered on simultaneously.
At 16nm up to 46% of a chip might need to be powered down, i.e. stay dark.

The dark silicon trend further increases the importance of pioneering research in power management.
Our goal is to research and build novel power management systems which will ensure that Moore's law can live on.

Motivational literature:
Dark Silicon and the End of Multicore Scaling
Intels tick tock seemingly dead becomes process architecture optimization


  • Santiago Pagani, Lars Bauer, Qingqing Chen, Elisabeth Glocker, Frank Hannig, Andreas Herkersdorf, Heba Khdr, Anuj Pathania, Ulf Schlichtmann, Doris Schmitt-Landsiedel, Mark Sagi, Éricles Sousa, Philipp Wagner, Volker Wenzel, Thomas Wild, Jörg Henkel: Dark silicon management: an integrated and coordinated cross-layer approach. it - Information Technology 58 (6), 2016, 297–307 mehr… BibTeX Volltext ( DOI )