Activity: Participating in or organising an event › Organising a conference, workshop, ...
Deep networks with billions of parameters trained on large datasets have achieved unprecedented success in various applications, ranging from medical diagnostics to urban planning and autonomous driving, to name a few. However, training large models is contingent on exceptionally large and expensive computational resources. Such infrastructures consume substantial energy, produce a massive amount of carbon footprint, and often soon become obsolete and turn into e-waste. While there has been a persistent effort to improve the performance of machine learning models, their sustainability is often neglected. This realization has motivated the community to look closer at the sustainability and efficiency of machine learning, by identifying the most relevant model parameters or model structures. In this workshop, we examine the community’s progress toward these goals and aim to identify areas that call for additional research efforts. In particular, by bringing researchers with diverse backgrounds, we will focus on the limitations of existing methods for model compression and discuss the tradeoffs among model size and performance.
Workshop website