As the computing paradigm shifts from cloud computing to end-edge-cloud computing, it
also supports artificial intelligence evolving from a centralized manner to a distributed one …

Object detection is a fundamental building block of video analytics applications. While
Neural Networks (NNs)-based object detection models have shown excellent accuracy on …

Concurrent inference execution on heterogeneous processors is critical to improve the
performance of increasingly heavy deep learning (DL) models. However, available …

Mobile GPU, as a ubiquitous and powerful accelerator, plays an important role in
accelerating on-device DNN (Deep Neural Network) inference. The frequent-upgrade and …

Mobile devices are increasingly equipped with heterogeneous multiprocessors, eg, CPU+
GPU+ DSP. Yet existing Neural Network (NN) inference fails to fully utilize the computing …

Mobile GPUs are extremely under-utilized for DNN computations across different mobile
deep learning frameworks and multiple DNNs with various complexities. We explore the …

More specialized chips are exploiting available high transistor density to expose parallelism
at a large scale with more intricate instruction sets. This paper reports on a compilation …

Executing deep neural networks (DNNs) on edge artificial intelligence (AI) devices enables
various autonomous mobile computing applications. However, the memory budget of edge …

针对遥感图像在轨实时处理提出一种基于嵌入式CPU+ GPU 异构平台的遥感图像滤波加速设计
方法. 以加速拉普拉斯滤波为例, 利用GPU 的并行计算特点, 通过数据划分及数据映射的方法对 …

Object detection, image classification, and various other computer vision tasks have become
prevalent on mobile devices. These computer vision tasks are typically executed with three …