As Internet of Things (IoT) keeps growing, IoT-side intelligence services, such as intelligent
personal assistant, healthcare surveillance, and smart home service, offload more and more …

With the increasing integration of Machine Learning (ML) applications into cloud services,
providing high throughput Machine Learning inference serving has become a major …

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 …

With the sustained technological advances in machine learning (ML) and the availability of
massive datasets recently, tech companies are deploying large ML-as-a-Service (MLaaS) …

Current trends in Machine Learning~(ML) inference on hardware accelerated devices (eg,
GPUs, TPUs) point to alarmingly low utilization. As ML inference is increasingly time …

Recent rapid development in deep learning (DL) applications generates harsh requirements
for DL inference services provided by GPU servers. On one hand, a high volume of different …

The proportion of machine learning (ML) inference in modern cloud workloads is rapidly
increasing, and graphic processing units (GPUs) are the most preferred computational …

To accelerate the training of Deep Learning (DL) models, clusters of machines equipped
with hardware accelerators such as GPUs are leveraged to reduce execution time. State-of …

Deep learning (DL) has been applied in billions of mobile devices due to its astonishing
performance in image, text, and audio processing. However, limited by the computing …

Issues related to operating Graphic Processing Unit (GPU) applications efficiently and
improving overall system throughput in a GPU cluster environment exist. The platform may …