The emergence of computational storage platforms like Delilah has transformed the data storage landscape, enabling new computing paradigms and facilitating data-intensive applications. Delilah is a cutting-edge computational storage platform developed by the IT University of Copenhagen. It runs on the Daisy OpenSSD and exposes an asynchronous computational storage protocol to the host, facilitated …
Supervisors: Niclas Hedam, Philippe Bonnet
Semester: Fall 2023
Tags: Open Source, Testing, Computational Storage, Hardware, FPGA

(This project will be carried out in collaboration with Xilinx Research Labs in Dublin) Machine Learning operators are becoming increasingly commonly used in data management systems and, in this project, we will explore the challenges and benefits of integrating inference operators from FINN [1] within a so-called Smart Storage system [2]. Both the inference and data management aspects will be …
Supervisors: Zsolt István
Semester: Spring 2021
Tags: FPGA, Data Management, MachineLearning

Modern data analytics systems are composed of two types of nodes: compute and storage (e.g., Amazon S3, Redis, MongoDB, etc.). The storage nodes typically offer a key-value interface and are often used to store data encoded in a columnar format (e.g., Parquet files). Due to growing data sizes in datacenters, there is an increasing interest in using specialized hardware devices, namely Field …
Supervisors: Zsolt István
Semester: Spring 2021
Tags: FPGA, Hardware-software Co-design, Security and Privacy

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 at the level of an SSD Flash Translation Layer, at the level of a Database storage manager or at the level of the database client. You will be able to experiment with FPGAs in the lab and on AWS. We …
Supervisor: Philippe Bonnet
Semester: Fall 2020
Tags: FPGA, SSD, computational storage