A large portion of current cloud and edge workloads feature Machine Learning (ML) tasks,
thereby requiring a deep understanding of their energy efficiency. While the holy grail for …

Machine learning (ML) has seen tremendous advancements, but its environmental footprint
remains a concern. Acknowledging the growing environmental impact of ML this paper …

Machine-learning (ML) hardware and software system demand is burgeoning. Driven by ML
applications, the number of different ML inference systems has exploded. Over 100 …

Deep learning inference at the edge continues to deliver state of the art results on real-world
applications involving images, video, speech, and human activity. The workhorse behind …

Recent breakthroughs in Machine Learning (ML) applications, and especially in Deep
Learning (DL), have made DL models a key component in almost every modern computing …

Energy efficiency is a critical concern for machine learning (ML) algorithms deployed in data
centers. Recently, many works in the literature have focused on running ML algorithms on …

Today, deep learning optimization is primarily driven by research focused on achieving high
inference accuracy and reducing latency. However, the energy efficiency aspect is often …

We propose a learning algorithm to design a lightweight neural multiplexer that given the
input and computational resource requirements, calls the model that will consume the …

Recent breakthrough technological progressions of powerful mobile computing resources
such as low-cost mobile GPUs along with cutting-edge, open-source software architectures …

Today, deep learning optimization is primarily driven by research focused on achieving high
inference accuracy and reducing latency. However, the energy efficiency aspect is often …