Chiplets are transforming computer system designs, allowing system designers to combine
heterogeneous computing resources at unprecedented scales. Breaking larger, mono-lithic …

Nowadays, the management of resource contention in shared cloud remains a pending
problem. The evolution and deployment of new application paradigms (eg, deep learning …

With DNN turning into the backbone of AI cloud services and propelling the emergence of
INFerence-as-a-Service (INFaaS), DNN-specific accelerators have become the …

Modern accelerators like GPUs are increasingly executing independent operations
concurrently to improve the device's compute utilization. However, effectively harnessing it …

A common strategy for improving efficiency in training deep learning entails multiplexing
tasks on a single GPU. To mitigate the interference caused by multiplexing, existing …

As Graphics Processing Units (GPUs) evolved into popular hardware accelerators for many
compute-hungry applications in the cloud, GPU virtualization has become a highly desirable …

As GPUs scale their low-precision matrix math throughput to boost deep learning (DL)
performance, they upset the balance between math throughput and memory system …

Data movement latency when using on-chip accelerators in emerging heterogeneous
architectures is a serious performance bottleneck. While hardware/software mechanisms …

NVIDIA's Multi-Instance GPU (MIG) is a feature that enables system designers to reconfigure
one large GPU into multiple smaller GPU slices. This work characterizes this emerging GPU …

As the popularity and relevance of the Function-as-a-Service (FaaS) model keeps growing,
we believe newer avatars of the service will support computationally intensive SIMT …