The estimation and optimization of energy consumption and carbon footprint derived from the use of AI systems. In this line, we will foster change in the entire lifecycle of AI products (i.e., idea generation, training, re-tuning, inference, etc.) by characterizing the use of ICT towards greater ecological integrity. The final goal is to develop AI technologies (particularly deep learning) that are compatible with sustaining environmental resources by exploring the trade-offs of accuracy of AI outcomes vs. reducing carbon emissions and computing power.
AI-Driven Classifier Building Pipeline
The creation of cryptocurrency systems that not only does not consume a lot of energy but can help save energy. The target is to create a new generation of cryptocurrency systems that can reward users who plant trees, and the underlying infrastructure does not use expensive consensus protocols.
Code Sanitization for Vulnerability Pruning and Exploitation Mitigation
The application of ICT and cloud computing technologies to support the creation of sustainable digital twins, including IoT data collection, to model cities and transportation systems. The target is to bring ICT as an enabler of a progressive systemic change towards developing an entire value chain to address circularity and systematic carbon footprint reduction.
The creation of sustainable ICT approaches and strategies to support the required massive computational and storage resources of different strategic scientific domains for our society with cloud computing. In particular, we will consider climate simulation, particles collider, and the Square Kilometer Array (SKA) challenges in the ETH domain.
Genomic and Reconfigurable Computing