Here you can see a list of all currently proposed projects. For a list of all previous proposals, see the proposal archive
Reproducibility is a cornerstone of the scientific method. There are systems available today to build reproducible and sharable data and analysis pipelines including workflow engines (e.g., GWL, …
Supervisors:
Philippe Bonnet
Semester: Fall 2020
Tags: reproducibility, workflow, HPC
Reproducibility is a cornerstone of the scientific method. It is also a core element of compliance requirements for sensitive equipment, e.g., audit trails for medical equipment. Often, a prerequisite …
Supervisors:
Philippe Bonnet
Semester: Fall 2020
Tags: reproducibility, edge
SSDs are not a uniform class of devices. SSD landscape is quite diverse now with many new-gen much faster / customizable devices being available. Understand their performance characteristics is …
Supervisors:
Pınar Tözün, Philippe Bonnet
Semester: Fall 2020
Tags: SSD, benchmarking
Disaggregated storage has gained acceptance in data centers. With disaggregated storage, storage resources are decoupled from compute resources, and made available through fabric. We are particularly …
Supervisors:
Philippe Bonnet, Pınar Tözün
Semester: Fall 2020
Tags: benchmarking, ARM, SoC, fabric, SSD, computational storage
The work on running data-intensive applications on very powerful, expensive, and power-hungry server hardware is very popular thanks to the growing size of data centers and high-performance computing …
Supervisors:
Philippe Bonnet, Pınar Tözün
Semester: Fall 2020
Tags: edge, NVIDIA Jetson, Raspberry Pi, Odroid
Field Programmable Gate Arrays are now an integral part of public cloud infrastructures. You can for example run customized FPGA instances on AWS. A project focuses on FPGA-based hardware acceleration …
Supervisor: Philippe Bonnet
Semester: Fall 2020
Tags: FPGA, SSD, computational storage
The goal of this project is ensure diversity in the relevance feedback results for the Exquisitor system, to improve quality of the user experience. A particularly interesting project for students …
Supervisor: Björn Þór Jónsson
Semester: Fall 2020
Tags: multimedia analytics, scalability
The index structure used for Exquisitor is eCP, a very scalable index for high-dimensional retrieval. While multimedia collections are typically constantly updated, however, the index is unfortunately …
Supervisor: Björn Þór Jónsson
Semester: Fall 2020
Tags: index maintenance, high-dimensional indexing
In this project, we propose to implement media server and media browser encapsulating a new data model for analysing media collections, called Multimedia Analytics Data Services (MADS). To validate …
Supervisor: Björn Þór Jónsson
Semester: Fall 2020
Tags: multimedia analytics, scalability
Students at ITU have made a prototype version of the Exquisitor system for the Android mobile phone! The system is missing some of Exquisitor’s advanced functionality, such as search and indexing, and …
Supervisor: Björn Þór Jónsson
Semester: Fall 2020
Tags: multimedia analytics, android
The goal of this project is to enhance Exquisitor as a competior for the Video Browser Showdown, an international video retrieval competition where competing systems are judged based on speed, …
Supervisor: Björn Þór Jónsson
Semester: Fall 2020
Tags: video search, multimedia analytics
We propose to develop a new prototype for analysing large multimedia collections in Virtual Reality, using the new Valve Indexes. There are many ways in which students can contribute to the project, …
Supervisor: Björn Þór Jónsson
Semester: Fall 2020
Tags: virtual reality, multimedia analytics
SSDs are not a uniform class of devices. SSD landscape is quite diverse now with many new-gen much faster / customizable devices being available. Understand their performance characteristics is …
Supervisors:
Pınar Tözün, Philippe Bonnet
Semester: Fall 2020
Tags: SSD, benchmarking
Disaggregated storage has gained acceptance in data centers. With disaggregated storage, storage resources are decoupled from compute resources, and made available through fabric. We are particularly …
Supervisors:
Philippe Bonnet, Pınar Tözün
Semester: Fall 2020
Tags: benchmarking, ARM, SoC, fabric, SSD, computational storage
The work on running data-intensive applications on very powerful, expensive, and power-hungry server hardware is very popular thanks to the growing size of data centers and high-performance computing …
Supervisors:
Philippe Bonnet, Pınar Tözün
Semester: Fall 2020
Tags: edge, NVIDIA Jetson, Raspberry Pi, Odroid
The variety and complexity of data-intensive applications and systems have been increasing drastically the past decade. Tasks from a SQL-based big data analytics request running on Apache Spark can be …
Supervisor: Pınar Tözün
Semester: Fall 2020
Tags: hardware-software co-design
Improvements in modern processor hardware does not automatically enable more complex and higher quality games, animations, and virtual reality applications. A paradigm shift is necessary when it comes …
Supervisor: Pınar Tözün
Semester: Fall 2020
Tags: memory hierarchy, concurrency
A data science infrastructure orchestrates the execution of widely used machine learning frameworks (e.g., TensorFlow , PyTorch) on a heterogeneous set of processing units (e.g., CPU, GPU, TPU, FPGA) …
Supervisor: Pınar Tözün
Semester: Fall 2020
Tags: benchmarking, hardware resource consumption, deep learning frameworks