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 …

With the rapid growth in the volume of data sets, models, and devices in the domain of deep
learning, there is increasing attention on large-scale distributed deep learning. In contrast to …

This paper presents a solution to the challenge of mitigating carbon emissions from hosting
large-scale machine learning (ML) inference services. ML inference is critical to modern …

Deep neural networks (DNNs) have been widely adopted for various mobile inference tasks,
yet their ever-increasing computational demands are hindering their deployment on …

With rapidly increasing distributed deep learning workloads in large-scale data centers,
efficient distributed deep learning framework strategies for resource allocation and workload …

As the applications of AI proliferate, it is critical to increase the throughput of online DNN
inference services. Multi-process service (MPS) improves the utilization rate of GPU …

Deep Neural Network (DNN) inference on serverless functions is gaining prominence due to
its potential for substantial budget savings. Existing works on serverless DNN inference …

In this work, we optimize the scheduling of Deep Learning (DL) training jobs from the
perspective of a Cloud Service Provider running a data center, which efficiently selects …

Computing is an indispensable tool in addressing climate change, but it also contributes to a
significant and steadily increasing carbon footprint, partly due to the exponential growth in …

GPUs have become the defacto hardware devices for accelerating Deep Neural Network
(DNN) inference workloads. However, the conventional sequential execution mode of DNN …