The global transport sector, a significant contributor to energy consumption and greenhouse
gas (GHG) emissions, requires innovative solutions to meet sustainability goals. Artificial …

The compute requirements associated with training Artificial Intelligence (AI) models have
increased exponentially over time. Optimisation strategies aim to reduce the energy …

Generative artificial intelligence (AI) technology is revolutionizing the computing industry.
Not only its applications have broadened to various sectors but also poses new system …

Integrating 3D magnetic resonance imaging (MRI) with machine learning has shown
promising results in healthcare, especially in detecting Alzheimer's Disease (AD). However …

Cloud computing is a significant and growing cause of carbon emissions. Understanding the
energy consumption and carbon footprints of cloud applications is a fundamental …

Artificial intelligence (AI) coupled with existing Internet of Things (IoT) enables more
streamlined and autonomous operations across various economic sectors. Consequently …

Dramatic increases in the capabilities of neural network models in recent years are driven by
scaling model size, training data, and corresponding computational resources. To develop …

AI workloads, often hosted in multi-tenant cloud environments, require vast computational
resources but suffer inefficiencies due to limited tenant-provider coordination. Tenants lack …

We provide a distillation scaling law that estimates distilled model performance based on a
compute budget and its allocation between the student and teacher. Our findings reduce the …

The energy demand of modern cloud services, particularly those related to generative AI, is
increasing at an unprecedented pace. While hyperscalers are collectively failing to meet …