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 …

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 …

Despite existing work in machine learning inference serving, ease-of-use and cost efficiency
remain challenges at large scales. Developers must manually search through thousands of …

As machine learning (ML) techniques are applied to a widening range of applications, high
throughput ML inference serving has become critical for online services. Such ML inference …

Performing Deep Neural Network (DNN) computation on hardware accelerators efficiently is
challenging. Existing DNN frameworks and compilers often treat the DNN operators in a …

The increasing demand for cloud-based inference services requires the use of Graphics
Processing Unit (GPU). It is highly desirable to utilize GPU efficiently by multiplexing different …

Deep learning training on a shared GPU cluster is becoming a common practice. However,
we observe severe sharing anomaly in production multi-tenant clusters where jobs in some …

With its huge real-world demands, large-scale confidential computing still cannot be
supported by today's Trusted Execution Environment (TEE), due to the lack of scalable and …

Machine learning models are widely deployed in production cloud to provide online
inference services. Efficiently deploying inference services requires careful tuning of …

Over the last few years, the ever-increasing use of Graphic Processing Units (GPUs) in
safety-related domains has opened up many research problems in the real-time community …