Bachelorarbeiten

Gegenstand dieser Bachelorarbeit ist die Entwicklung eines Compile-Flows, mit dem verschiedene Benchmarks, z.B: von EEMBC, kompiliert und auf einer SystemC basierten Prototyp Architektur abgespielt werden können. Dabei sollen verschiedene Benchmarks, ggf. mit unterschiedlichen Parametern so in das System eingebunden werden, dass jedes Teammitglied diese auf einfache Weise kompilieren und abspielen kann.

Das SystemC Modell verwendet ein taktgenaues Modell eines Prozessors der Synopsys ARC Familie, um Speicherzugriffe auszuführen und so die Speicherhierarchie unter realistischen Bedingungen zu testen und zu evaluieren.

Je nach zeitlichem Fortgang der Arbeit kann man die Ergebnisse der Benchmarks dann  auswerten

• Gutes Fachwissen über MPSoC Systeme
• Kenntnisse über Python-Programmierung
• Hohe Motivation
• Selbstverantwortliche Arbeitsweise

This thesis entails a comprehensive study and comparison of the various Inter-Process Communication (IPC) mechanisms provided by the Linux operating system. IPC is a fundamental concept in operating systems, enabling processes to communicate, synchronize, and share data. This is especially crucial in multi-process and distributed computing environments, where seamless data exchange and coordination are key to system efficiency. The project begins by exploring several widely used IPC mechanisms in Linux, such as futexes, epoll, semaphores, sockets, and io_uring. Each of these mechanisms serves distinct purposes and use cases, and this study will investigate their specific roles, functionality, and how they can be employed in various application contexts.

A key part of the work will involve evaluating these mechanisms based on several critical factors:

• Performance:  Detailed analysis of each mechanism's speed, efficiency, and resource consumption, focusing on latency and throughput under different conditions.
• Ease of Implementation:  A review of how straightforward or complex it is to implement these mechanisms, considering factors such as the required setup, programming effort, and maintainability.
• Use-Case Suitability:  Examining the appropriateness of each IPC mechanism for specific scenarios.

The practical component of the project will include developing sample applications that utilize each IPC mechanism. This will be followed by benchmarking tests to measure performance in real-world-like conditions. The project will offer concrete insights into the trade-offs between different mechanisms by executing these tests. The final deliverable will provide a thorough comparative analysis, synthesizing the results of the experiments and assessments. Based on this analysis, recommendations will be made regarding the most appropriate IPC mechanisms for specific Linux-based applications, such as server-client architectures, parallel computing environments, or embedded systems. This project aims to serve as a valuable reference for developers, system administrators, and architects who need to make informed choices about IPC in their systems.

At LIS, we try to leverage the Duckietown hardware and software ecosystem to experiment with our reinforcement learning (RL) agents, known as learning classifier tables (LCTs), as part of the Duckiebots' control system (https://www.ce.cit.tum.de/lis/forschung/aktuelle-projekte/duckietown-lab/).
More information on Duckietown can be found at https://www.duckietown.org/.
In this student work, steering Duckiebots should be realized via LCTs. Therefore, a Python implementation of the RL agent needs to be included in the Duckietown pipeline. Replacing the current controller with an RL-based one involves observing suitable sensor values and selecting reasonable actions. Different reward functions and learning methods are to be implemented and evaluated regarding their resulting performance and efficiency.
The thesis aims to shift the vehicle steering entirely to the new RL-based approach, ideally reducing computation effort.

• Experience with Python and ROS
• Basic knowledge of reinforcement learning
• Structured way of working and problem-solving skills

At LIS, we try to leverage the Duckietown hardware and software ecosystem to experiment with our reinforcement learning (RL) agents, known as learning classifier tables (LCTs), as part of the Duckiebots' control system (https://www.ce.cit.tum.de/lis/forschung/aktuelle-projekte/duckietown-lab/).
More information on Duckietown can be found at https://www.duckietown.org/.
In this student work, the control of driving speed and platooning distance should be realized via LCTs. Therefore, a Python implementation of the RL agent needs to be included in the Duckietown pipeline. Replacing the current controller with an RL-based one involves observing suitable sensor values and selecting reasonable actions. Different reward functions and learning methods are to be implemented and evaluated regarding their resulting performance and efficiency.
The thesis aims to shift the speed and platooning control entirely to the new RL-based approach, ideally reducing computation effort.

• Experience with Python and ROS
• Basic knowledge of reinforcement learning
• Structured way of working and problem-solving skills

Gegenstand dieser Bachelorarbeit ist die Entwicklung eines Python-Tools, welches verschiedene Statistiken über die Speicherzugriffe einer MPSoC-Architektur erstellt. Dazu werden simulations-basierte Traces verwendet, in denen alle Speicherzugriffe aufgezeichnet werden. In diesen Traces sind alle Zugriffe dokumientiert: Zeitpunkt? Cache Hit/Miss? Welcher Core?

Aus diesen Traces sollen verschiedene Statistiken erstellt werden, dazu muss ein Python Programm geschrieben werden, welches die Traces auswertet und Plottet.

Mögliche Statistiken sind beispielsweise

• Auf welche Page wurde wie oft zugegriffen?
• Wie viele Zugriffe hintereinander fallen im Schnitt in die selbe Page
• Wie ist die zeitliche Verteilung der unterschiedlichen Pages?
• Zeitlicher Abstand zwischen Zugriffen auf dieselbe Page?

Diese Daten sollen bei der Analyse von Speicherzugriffsmustern von verschiedenen Anwendungen helfen, um so einen effizienten Mechanismus zum Vorladen ausgewählter Speicherinhalte zu entwickeln.

• Gutes Fachwissen über MPSoC Systeme
• Kenntnisse über Python-Programmierung
• Hohe Motivation
• Selbstverantwortliche Arbeitsweise

With the advent of research on the next generation of
mobile communications 6G, we are engaged in exploring
architecture extensions for Smart Network Interface Cards
(SmartNICs). To enable adaptive, energy-efficient and
low-latency network interfaces, we are prototyping a
custom packet processing pipeline on FPGA-based NICs,
partially based on the open-nic project
(https://github.com/Xilinx/open-nic).

To properly evaluate the performance of a
SmartNIC-assisted server, a webserver-based application
shall be setup in a Linux OS environment and tested with different request rates. Additionally, the performance of single- vs. multicore platforms for one and multiple webserver instances should be compared. CPU core isolation mechanisms can be used to setup such scenarios on the server.

The goal of this work is to setup a webserver (e.g. NGINX), potentially with a database backend (e.g. MongoDB) and develop a measurement and testing methodology for performance benchmarking of a SmartNIC-assisted server. This includes throughput and latency measurements, as well as analysis of the CPU and network utilization.

•     Programming skills C and Python
•     Good Knowledge of computer networks, OSI layer model and protocols
•     Comfortable with the Linux command line and bash
•     Preferably experience with Linux drivers and low-level software