Computer vision applications, especially those using augmented reality technology, are becoming quite popular in mobile devices. However, this type of application is known as presenting significant demands regarding resources. In order to enable its utilization in devices with more modest resources, edge computing can be used to offload certain high intensive tasks. Still, edge computing is usually composed of devices with limited capacity, which may impact in users quality of experience when using computer vision applications. This work proposes the use of embedded devices with graphics processing units (GPUs) to overcome such limitation. Experiments performed shown that GPUs can attain a performance gain when compared to using only CPUs, which guarantee a better experience to users using such kind of application.
A dynamic memory assignment strategy for dilation-based ICP algorithm on embedded GPUs
School of Coumputing Institute of Science（计算学院科学研究所）；Dalian University of Technology（大连理工大学）；University of Wollongong (College HK)（卧龙岗大学（香港学院））；School of Computing Institute of Science（计算学院科学研究所）
StereoVAE: A lightweight stereo-matching system using embedded GPUs
School of Computing, Tokyo Institute of Technology（东京技术大学计算机学系）；School of Electronic Science & Engineering, Nanjing University（南京大学电子科学与工程学院）；Artificial Intelligence Research Center, National Institute of Advanced Industrial Science and Technology (AIST)（国家先进工业科学与技术研究院人工智能研究中心）
Topic modeling has found wide application in many problems where latent structures of the data are crucial for typical inference tasks. When applying a topic model, a relatively standard pre-processing step is to first build a vocabulary of frequent words. Such a general pre-processing step is often independent of the topic modeling stage, and thus there is no guarantee that the pre-generated vocabulary can support the inference of some optimal (or even meaningful) topic models appropriate for a given task, especially for computer vision applications involving "visual words". In this paper, we…
Optimizing Sparse Convolution on GPUs with CUDA for 3D Point Cloud Processing in Embedded Systems
Brion Technologies, an ASML Company（Brion Technologies（ASML旗下公司））；School of Mechanical Engineering and Automation, Northeastern University（东北大学机械工程与自动化学院）
该研究针对嵌入式系统中的 3D 点云处理，指出点云稀疏且无规则网格所带来的计算挑战，并聚焦于利用 CUDA 优化 GPU 上的稀疏卷积。
Chongqing University of Science and Technology（重庆科技学院）
Real-time object detection on Unmanned Aerial Vehicles (UAVs) is a challenging issue due to the limited computing resources of edge GPU devices as Internet of Things (IoT) nodes. To solve this problem, in this paper, we propose a novel lightweight deep learning architectures named FasterX based on YOLOX model for real-time object detection on edge GPU. First, we design an effective and lightweight PixSF head to replace the original head of YOLOX to better detect small objects, which can be further embedded in the depthwise separable convolution (DS Conv) to achieve a lighter head. Then, a slim…
With the improvements in the object detection networks, several variations of object detection networks have been achieved impressive performance. However, the performance evaluation of most models has focused on detection accuracy, and performance verification is mostly based on high-end GPU hardware. In this paper, we propose real-time object detectors that guarantee balanced performance for real-time systems on embedded platforms. The proposed model utilizes the basic head structure of the RefineDet model, which is a variant of the single-shot object detector (SSD). In order to ensure real-…
Fraunhofer Institute of Optronics, System Technologies and Image Exploitation (IOSB)（弗劳恩霍夫光学、系统技术与图像 exploitation 研究所）；Fraunhofer Center for Machine Learning（弗劳恩霍夫机器学习中心）；Karlsruhe Institute of Technology (KIT)（卡尔斯鲁厄理工学院）；Institute of Photogrammetry and Remote Sensing, Karlsruhe Institute of Technology (KIT)（卡尔斯鲁厄理工学院摄影测量与遥感研究所）；Vision and Fusion Laboratory, Karlsruhe Institute of Technology (KIT)（卡尔斯鲁厄理工学院视觉与融合实验室）
With the emergence of low-cost robotic systems, such as unmanned aerial vehicle, the importance of embedded high-performance image processing has increased. For a long time, FPGAs were the only processing hardware that were capable of high-performance computing, while at the same time preserving a low power consumption, essential for embedded systems. However, the recently increasing availability of embedded GPU-based systems, such as the NVIDIA Jetson series, comprised of an ARM CPU and a NVIDIA Tegra GPU, allows for massively parallel embedded computing on graphics hardware. With this in min…
Iowa State University（爱荷华州立大学）；Xilinx Research Labs（赛灵思研究实验室）
Developing high performance embedded vision applications requires balancing run-time performance with energy constraints. Given the mix of hardware accelerators that exist for embedded computer vision (e.g. multi-core CPUs, GPUs, and FPGAs), and their associated vendor optimized vision libraries, it becomes a challenge for developers to navigate this fragmented solution space. To aid with determining which embedded platform is most suitable for their application, we conduct a comprehensive benchmark of the run-time performance and energy efficiency of a wide range of vision kernels. We discuss…
LangSurf: Language-Embedded Surface Gaussians for 3D Scene Understanding
Brain and Artificial Intelligence Lab, Northwestern Polytechnical University（脑科学与人工智能实验室，西北工业大学）；Tsinghua University（清华大学）；Shanghai Jiaotong University（上海交通大学）；Peking University（北京大学）；School of Artifical Intelligence, Chongqing University of Posts and Telecommunications（人工智能学院，重庆邮电大学）
As a type of prominent studies in deep learning, generative models have been widely investigated in research recently. Two research branches of the deep learning models, the Generative Networks (GANs, VAE) and the Semantic Segmentation, rely highly on the upsampling operations, especially the transposed convolution and the dilated convolution. However, these two types of convolutions are intrinsically different from standard convolution regarding the insertion of zeros in input feature maps or in kernels respectively. This distinct nature severely degrades the performance of the existing deep …
Deep Learning (DL) has become a crucial technology for Artificial Intelligence (AI). It is a powerful technique to automatically extract high-level features from complex data which can be exploited for applications such as computer vision, natural language processing, cybersecurity, communications, and so on. For the particular case of computer vision, several algorithms like object detection in real time videos have been proposed and they work well on Desktop GPUs and distributed computing platforms. However these algorithms are still heavy for mobile and embedded visual applications. The rap…
