Deep learning (DL) shows its prosperity in a wide variety of fields. The development of a DL
model is a time-consuming and resource-intensive procedure. Hence, dedicated GPU …

Traditional deep learning frameworks such as TensorFlow and PyTorch support training on
a single deep neural network (DNN) model, which involves computing the weights iteratively …

Deep learning (DL) has revolutionized unstructured data analytics. But in most cases, DL
needs massive labeled datasets and large compute clusters, which hinders its adoption …

Driven by the tremendous effort in researching novel deep learning (DL) algorithms, the
training cost of developing new models increases staggeringly in recent years. We analyze …

Large language models such as GPT-3 & ChatGPT have transformed deep learning (DL),
powering applications that have captured the public's imagination. These models are rapidly …

Compared to conventional neural networks, training a supernet for Neural Architecture
Search (NAS) is very time consuming. Although current works have demonstrated that …

Recent advances in computer architecture and networking have ushered in a new age of
edge computing, where computation is placed close to the point of data collection to …

With the development of the architectures and the growth of AIoT application requirements,
data processing on edge has become popular. Neural network inference is widely employed …

Increasingly modern computing applications employ progressive iterative analytics, as best
exemplified by two prevalent cases, approximate query processing (AQP) and deep learning …

In modern cloud environments, ephemeral resources with intermittent availability and
fluctuating monetary costs are becoming common. This dynamic nature presents a new …