https://dasya.itu.dk/supervisor/robert-bayer/ Recent content in Robert Bayer on DASYA Hugo -- gohugo.io en-us © DASYA, IT University of Copenhagen Tue, 15 Jul 2025 00:00:00 +0000 https://dasya.itu.dk/for-students/proposals/archive/pito-tiny-resourcemanagement/ Tue, 15 Jul 2025 00:00:00 +0000 https://dasya.itu.dk/for-students/proposals/archive/pito-tiny-resourcemanagement/ Today many data sources are small low-powered and hardware-constrained devices such as mobile phones, wearable or self-driving smart platforms, etc. Edge computing is a broad term that refers to computations performed on such edge devices. It becomes increasingly important to enable techniques that get more value out of data at the edge rather than always sending the data to a remote and more powerful hardware device in the cloud for further data processing and training machine learning models. https://dasya.itu.dk/for-students/proposals/archive/pito-tiny-modelupdates/ Mon, 13 May 2024 00:00:00 +0000 https://dasya.itu.dk/for-students/proposals/archive/pito-tiny-modelupdates/ One of the key challenges with enabling efficient machine learning on resource-constrained devices is keeping the machine learning models deployed on these devices up-to-date without frequent retraining. This requires exploring the impact of different model update mechanisms at the edge. This project would be suitable as a standalone project or BSc or MSc thesis at ITU during Fall 2024. If you are interested in resource-constrained hardware, benchmarking, and machine learning in general, this project would be a great fit for you. https://dasya.itu.dk/for-students/proposals/archive/pito-tiny-modelsizes/ Mon, 13 May 2024 00:00:00 +0000 https://dasya.itu.dk/for-students/proposals/archive/pito-tiny-modelsizes/ To enable efficient data processing and machine learning on resource-constrained devices has many challenges. One is fitting the models into the restrictive memory and compute resources of these devices. In this project, first, we would like to explore the landscape of foundational, generative-AI, language, etc. models with respect to their size and compute needs to understand what could be a fit to be deployed at the edge. Then, we would like to characterize the performance of some edge model deployments on a chosen edge device and building on the analysis in the first step. https://dasya.itu.dk/for-students/proposals/archive/jucp-disco2-new-pipeline/ Sun, 13 Aug 2023 00:00:00 +0000 https://dasya.itu.dk/for-students/proposals/archive/jucp-disco2-new-pipeline/ The DISCO-2 satellite will have accelerated machine learning capability based on the inclusion of a Coral TPU ML accelerator module. This will allow images taken by the satellite to be analaysed on satellite using a variety of ML models, with only select images sent back to Earth. This approach allows for more flexibility in image aquisition and saves downlink bandwidth which is very constrained in the case of DISCO-2. This project will address the entire image pipeline including pre-processing of images on the satellite after aquisition for insertion into the ML pipeline, the selection of images based on the results of ML analysis, the transfer of images to the groundstation and the data storage and presentation of the images.