Zur Webseite der Uni Stuttgart

ES - Abschlussarbeiten

Offene Arbeiten

zur Zeit sind keine Themen verfügbar!



Laufende Arbeiten

STT-MRAM memories in embedded system design and optimization (Master Thesis)

Complexity in embedded system design is steadily increasing. This is due to miniaturization of logic, on the one hand, and the rising demand for computational power at minimal power budgets, on the other hand. To cope with these opposing design goals and related challenges, automated system design and optimization methods are of essential importance and subject to intensive research.

In this context, an ongoing research project at the Chair of Embedded Systems investigates automated design concepts and optimization methods for power-efficient memory subsystems. Main goal is the minimization of total memory subsystem energy comsumption, while respecting certain constraints in terms of time and power. Due to the predominant use of SRAM and partially DRAM memory in embedded systems, existing optimization methods have been developed with focus on these memory technologies.

With Spin Transfer Torque Magnetic Random Access Memory (STT-MRAM), lately a new type of memory technology is on the rise. Following the promises of the nanomagnetic research community, this technology is providing a solution to the main drawbacks of SRAM (volatility, large cell size, leakage) and DRAM (volatility, refresh current). Besides different critical parameters related to production, e.g. thermal stability or retention time, especially the write operation appears to be a promising target for design-time optimization at system level. Following the authors of, a variation of the memory cell’s write current allows the realization of different trade-off working points in terms of write energy and write latency.

Using this characteristic, a static and/or dynamic optimization concept shall be developed for the application in embedded system design. The resulting implementation shall allow for memory subsystem optimization with respect to low energy consumption and shall be integrated with the existing memory optimization workflow at the institute.

Student: Purnima Dutt

Supervisor: Manuel Strobel


Diese Webseite verwendet Cookies. Durch die Nutzung dieser Webseite erklären Sie sich damit einverstanden, dass Cookies gesetzt werden. Mehr erfahren, zum Datenschutz