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 …

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 …

Modern websites increasingly rely on machine learning (ML) to improve their business
efficiency. Developing and maintaining ML services incurs high costs for developers …

Recent advances in deep learning (DL) have spawned various intelligent cloud services
with well-trained DL models. Nevertheless, it is nontrivial to maintain the desired end-to-end …

GPU technology has been improving at an expedited pace in terms of size and performance,
empowering HPC and AI/ML researchers to advance the scientific discovery process …

Deep neural networks (DNNs) have become a critical component for inference in modern
mobile applications, but the efficient provisioning of DNNs is non-trivial. Existing mobile-and …

Deep Learning (DL) models have achieved superior performance. Meanwhile, computing
hardware like NVIDIA GPUs also demonstrated strong computing scaling trends with 2x …

Since emerging edge applications such as Internet of Things (IoT) analytics and augmented
reality have tight latency constraints, hardware AI accelerators have been recently proposed …

Augmented reality (AR) devices perform visual simultaneous localization and mapping
(SLAM) to map the real world and localize themselves in it, enabling them to render the …