Deep Learning (DL) models have achieved superior performance in many application
domains, including vision, language, medical, commercial ads, entertainment, etc. With the …

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

Cloud platforms today have been deploying hardware accelerators like neural processing
units (NPUs) for powering machine learning (ML) inference services. To maximize the …

Many applications such as autonomous driving and augmented reality, require the
concurrent running of multiple deep neural networks (DNN) that poses different levels of real …

AI-powered applications often involve multiple deep neural network (DNN)-based prediction
tasks to support application-level functionalities. However, executing multi-DNNs can be …

Immersive applications such as Augmented Reality (AR) and Mixed Reality (MR) often need
to perform multiple latency-critical tasks on every frame captured by the camera, which all …

Robot Operating System (ROS) is a prevailing software framework for robotic appliscation
development. Graphics Processing Unit (GPU) is widely used in many ROS applications as …

GPUs have become the defacto hardware devices for accelerating Deep Neural Network
(DNN) inference workloads. However, the conventional sequential execution mode of DNN …

Real-time perception is important for autonomous driving. We observe an emerging trend
using one large and critical fusion-based Bird's-Eye-View (BEV) Deep Neural Network …

DNNs are ubiquitous on edge devices nowadays. With its increasing importance and use
cases, it's not likely to pack all DNNs into device memory and expect that each inference has …