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 …

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 …

To accelerate the inference of machine-learning (ML) model serving, clusters of machines
require the use of expensive hardware accelerators (eg, GPUs) to reduce execution time …

GPUs have become the defacto hardware devices to accelerate Deep Neural Network
(DNN) inference in deep learning (DL) frameworks. However, the conventional sequential …

With the proliferation of the adoption of renewable energy in powering data centers,
addressing the challenges of such energy sources has attracted researchers from academia …

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

With the emergence of large foundational models, model-serving systems are becoming
popular. In such a system, users send the queries to the server and specify the desired …