The emerging field of artificial intelligence of things (AIoT, AI+ IoT) is driven by the
widespread use of intelligent infrastructures and the impressive success of deep learning …

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 …

Deep neural networks (DNNs) have achieved unprecedented success in the field of artificial
intelligence (AI), including computer vision, natural language processing, and speech …

During image editing, existing deep generative models tend to re-synthesize the entire
output from scratch, including the unedited regions. This leads to a significant waste of …

We study fusion for deep neural networks (DNNs) in a just-in-time (JIT) compilation
framework Apollo. It considers both memory-and compute-bound tensor operators for fusion …

With the fast development of deep neural networks (DNNs), many real-world applications
are adopting multiple models to conduct compound tasks, such as co-running classification …

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

ResNets are widely used in the intrusion detection system (IDS) of software-defined
industrial network to construct accurate intelligence detection of network attacks. However …

The deep learning (DL) compiler serves as a vital infrastructure component to enable the
deployment of deep neural networks on diverse hardware platforms such as mobile devices …

Neural network training requires a large amount of computation and thus GPUs are often
used for the acceleration. While they improve the performance, GPUs are underutilized …