»New paradigms for machine learning on non-volatile memory architectures«

Since many years, the Chair for Embedded Systems works internationally successful in the areas of computer engineering, such as smart embedded systems. Many interesting and open problems in these areas need to be addressed to successfully deploy such systems in modern application domains.

Embedded systems usually have limited energy, computing power, and memory/storage space. Hardware accelerators and revolutionary memory hierarchies are promising candidates to tackle these challenges. On the memory side, the emerging byte-addressable non-volatile memories (referred to as NVMs for short), such as Phase Change Memory, Spin-Transfer Torque RAM and Resistive RAM (ReRAM), feature low leakage power, high density, and low unit costs. NVMs are hence interesting alternatives to replace DRAM as main memory and/or hard disks and flash as storage. NVMs offer the opportunity for new memory architectures and open new computing paradigms like near-memory and memory computing.

In the scope of this research project, we want to investigate energy efficient machine learning applications on resource constraint devices/systems in which the underlying architectures profit from in-memory and/or near-memory computing.

• The candidate (f/m/d) must have a very good Master's degree or PhD degree (or equivalent) in CS or EE with background or specialization in some of following topics, i.e., NVM, machine learning for resource constraints systems, in-memory computing, near-memory computing.
• The ideal candidate (f/m/d) shows a strong interest and motivation to deepen in these topics to a level required for a doctorate or as a PostDoc.
• Experience and/or interest in C/C++, VHDL/Verilog, synthesis tools, and/or CPU/system simulators will be beneficial.
• Fluency in written and spoken English is a prerequisite.
• We are looking for a highly motivated candidate (f/m/d) with a strong commitment to research ethics and teamwork.
• Good communicative skills are mandatory due to the interdisciplinary structure of the project and the team.