Deep learning (DL) has demonstrated its remarkable success in a wide variety of fields. The
development of a DL model is a time-consuming and resource-intensive procedure. Hence …

Model parallelism is conventionally viewed as a method to scale a single large deep
learning model beyond the memory limits of a single device. In this paper, we demonstrate …

Large language models (LLMs) power a new generation of interactive AI applications
exemplified by ChatGPT. The interactive nature of these applications demands low latency …

In the rapidly evolving landscape of artificial intelligence (AI), generative large language
models (LLMs) stand at the forefront, revolutionizing how we interact with our data. However …

Many intelligent applications like autonomous driving and virtual reality require running both
latency-critical and best-effort DNN inference tasks to achieve both real time and work …

In a vertical federated learning (VFL) scenario where features and models are split into
different parties, it has been shown that sample-level gradient information can be exploited …

With the increasing popularity of large deep learning model-serving workloads, there is a
pressing need to reduce the energy consumption of a model-serving cluster while …

Deep learning (DL) is becoming increasingly popular in many domains, including computer
vision, speech recognition, self-driving automobiles, etc. GPU can train DL models efficiently …

DNN models across many domains continue to grow in size, resulting in high resource
requirements for effective training, and unpalatable (and often unaffordable) costs for …

Containers are widely used for resource management in datacenters. A common practice to
support deep learning (DL) training in container clouds is to statically bind GPUs to …