IBM Thesis Automated pre-silicon system performance analysis framework 

Germany, Baden-Württemberg 

131099891

The goal of this master thesis is to develop an automation solution for the analysis of the pre-silicon system-level performance regression results. It must assist the team of performance verification engineers in finding performance issues of next-generation IBM microprocessors the most efficient way.
Similar work has been started in another verification area, and this master thesis will analyze these first ideas and concepts, continue, and expand them – if they fit.

1. The first step would be to thoroughly analyze the existing data set and performance metrics and to apply the previously mentioned ideas.
2. The solution then needs to be optimized to the pre-silicon performance regression use-case, e.g. by applying further data science methods and/or developing new scores.
3. Eventually, AI and/or machine learning techniques can be applied to make the solution adaptive to changes throughout the project, or over the course of several project, respectively.

Required Technical and Professional Expertise

• Study of computer science, electrical engineering, or a similar direction
• Good knowledge of programming, knowledge of a scripting language like Python
• Knowledge of data science, statistics, and machine learning
• Knowledge of development tools / technologies (e.g. git)
• AI knowledge
• English or German language

Preferred Technical and Professional Expertise

• First experience in developing hardware, and performance analysis.