Quantifying the embodied carbon emissions of computer systems is a growing area of
interest and several models to do so have already been established. However, these …

Datacenters have been a significant source of global carbon emissions. Researchers have
developed tools to quantify the emissions of datacenters, aiming to enable environmentally …

Several 2-D architectures have been presented, including systolic arrays or compute-in-
memory (CIM) arrays for energy-efficient artificial intelligence (AI) inference. To increase the …

Spiking Neural Networks (SNNs) provide a brain-inspired and event-driven mechanism that
is believed to be critical to unlock energy-efficient deep learning. The mixture-of-experts …

The rise of Machine Learning (ML) and the wide use of Deep Neural Networks (DNNs) have
led to the development of specialized DNN accelerators, aimed at improving the computing …

Growing global concerns about climate change highlight the need for environmentally
sustainable computing. The ecological impact of computing, including operational and …

Quantifying uncertainty in carbon modeling is a growing area of interest, and researchers
have already developed uncertainty-aware carbon modeling tools. However, these tools …