Estimating Energy Consumption of a Deep Learning Architecture

Deep neural networks (DNNs) have succeeded remarkably in various AI applications, including computer vision, speech recognition, and machine translation. However, their high computational demands result in significant energy consumption, which raises operating costs for data centers and limits their deployment on mobile devices with constrained energy budgets. As local processing on mobile devices gains popularity due to privacy, security, and latency concerns, designing energy-efficient DNNs becomes essential for advancing mobile AI applications. Hence, in this thesis, we will focus on designing a tool or technique for measuring the energy consumption of the DL model.

Interactive Web Dashboard for Tartu Smart Bike Data Visualization and Analysis

Tartu Smart Bike (Tartu rattaringlus) is a bike-sharing system in Tartu, providing residents with an affordable and environmentally friendly travel option. Over its five years of operation, it has accumulated a significant amount of data, including trip-level and detailed GPS data. This data can aid researchers and policymakers in designing more bikeable cities. While parts of this data have been utilized in various research projects, there is a lack of a comprehensive tool that would offer an overview of the data spanning the entire period of the system’s operation. The aim of this thesis is to create a web-based dashboard to visualize and explore Tartu Smart Bike data. The dashboard should enable users to examine bike-sharing usage from both spatial and temporal perspectives and compare different time periods. Depending on the scope, a more in-depth analysis of GPS trajectories could be included. The outcome of this work should be a web application that facilitates user-friendly exploration of the Tartu Smart Bike system.

Enhanced sequence modeling using Mamba for Multi-Object Tracking

Multi-object tracking (MOT) is a fundamental task in the field of computer vision that involves tracking multiple objects across video frames. It extends beyond simple object detection by maintaining consistent identities for each object throughout a sequence, despite challenges such as occlusion, abrupt movements, and changes in appearance. This capability is crucial for applications requiring detailed analysis of object interactions and movements over time. On the other hand, Mamba is a cutting-edge deep learning architecture designed for efficient sequence modeling, particularly addressing the limitations of traditional Transformer models in handling long sequences. This project aims to revolutionize multi-object tracking (MOT) by integrating the Mamba architecture as a long-sequence memory model. The goal is to enhance the ability of MOT systems to handle complex motion patterns and occlusions, which are common challenges in dynamic environments such as video surveillance and autonomous vehicles.

Spiking Neural Network Vs. Transformers in Object Detection in Urban Scenes: A Race for Energy Efficiency

Object detection in city environments is critical for applications such as autonomous driving, intelligent surveillance, and urban planning. As the complexity and scale of these applications grow, so does the demand for computational resources, leading to increased energy consumption. Traditional Transformer-based models, while powerful, are often energy-intensive. On the other hand, Spiking Neural Networks (SNNs), inspired by biological neural processes, promise significant energy savings due to their event-driven nature. This project explores the potential of SNNs as a more energy-efficient alternative to Transformers. The aim is to analyze the comparative performance of Spiking Neural Networks (SNNs) and Transformer-based architectures in object detection within urban scenes. The energy efficiency analysis focuses on determining which neural network paradigm offers superior performance from both (accuracy and liability) while minimizing power consumption. By leveraging neuromorphic hardware and optimizing Transformer models, the research seeks to contribute to developing sustainable and efficient AI systems for real-time urban environment analysis.