Due to the limited compute power and storage capabilities of edge platforms, ''streaming'' often provides a better VR experience compared to ''rendering''. Yet, achieving high-quality VR streaming faces two significant challenges, namely, bandwidth limitations and the need for real-time operation with high frames per second (FPS). Previous efforts have tended to prioritize either conserving bandwidth without real-time performance or ensuring real-time operation without substantial bandwidth savings. In this work, we incorporate the concept of ''stereo similarity'' to develop a novel real-time stereo video compression framework for streaming, called Pirate. Unlike the previously proposed approaches that rely on large machine learning-based models for synthesizing stereo pairs from both eyes with disparity maps (which can be impractical for most edge platforms due to their high computational cost), Pirate iteratively synthesizes the target eye view using only a single eye view and its corresponding disparity and optical flow information, with alternating left or right eye transmission. This enables us to generate target view at an extremely low computational cost, even under bandwidth constraints as low as 0.1 bits per pixel (bpp), while maintaining a high frame rate of 90 FPS. Our evaluations also reveal that, the proposed approach not only achieves real-time VR streaming with a 20%-40% reduction in bandwidth usage, but also maintains similar superior quality standards.

In recent years, high dynamic range (HDR) content has become increasingly popular for its ability to represent a broader brightness range, enhancing the realism and immersion in applications like augmented reality/virtual reality (AR/VR) on edge devices. While DNN-based solutions are effective for reconstructing high-fidelity HDR content, due to their high computational demands and memory usage, the DNN-based HDR reconstruction takes up to several seconds to generate one HDR image, making it very challenging to deploy such techniques onto the edge devices.

With the emergence of GPUs as first-class compute engines, more concentrated focus has been put into covert and side channel discovery in these architectures. However, most of the covert and side channels uncovered on GPUs to date are rooted in “GPU cores”, which include computational cores, cache and core interconnects, but they do not consider “GPU uncore”, which include non-computational engines, GPU DRAM, host-G PU links and inter-GPulinks. In this paper, we delve into the less-explored domains of GPU uncore, unveiling four novel leakage sources for covert and side channel exploitation: (1) GPU DRAM frequency scaling; (2) NVENC utilization; (3) NVDEC utilization; (4) NVJPEG utilization. What makes these covert and side channels interesting is that they all take effect under the GPU MPS mode - which fractionalizes GPU cores and GPU memory on both desktop-scale and server-scale GPUs. Furthermore, our study reevaluates PCI-e bandwidth allocation on GPUs. Notably, we have engineered covert and side channel capable of bypassing GPU MIG isolation - a mechanism implemented by NVIDIA to physically segregate hardware resources on server-scale GPUs. Our research showcases concrete examples of these covert and side channels, highlighting their potency in breaching system security, all achieved without necessitating root privileges. This underscores the practical implications and urgency of addressing these vulnerabilities in GPU architectures.

Recently, point cloud (PC) has gained popularity in modeling various 3D objects (including both synthetic and real-life) and has been extensively utilized in a wide range of applications such as AR/VR, 3D reconstruction, and autonomous driving. For such applications, it is critical to analyze/understand the surrounding scenes properly. To achieve this, deep learning based methods (e.g., convolutional neural networks (CNNs)) have been widely employed for higher accuracy. Unlike the deep learning on conventional 2D images/videos, where the feature computation (matrix multiplication) is the major bottleneck, in point cloud-based CNNs, the sample and neighbor search stages are the primary bottlenecks, and collectively contribute to 54% (up to 80%) of the overall execution latency on a typical edge device. While prior efforts have attempted to solve this issue by designing custom ASICs or pipelining the neighbor search with other stages, to our knowledge, none of them has tried to "structurize" the unstructured PC data for improving computational efficiency.

Side-channel attacks are one of the rising security concerns in modern computing platforms. Observing this, researchers have proposed both hardware-based and software-based strategies to mitigate side-channel attacks, targeting not only on-chip caches but also other hardware components like memory controllers and on-chip networks. While hardware-based solutions to side-channel attacks are usually costly to implement as they require modifications to the underlying hardware, software-based solutions are more practical as they can work on unmodified hardware. One of the recent software-based solutions is constant-time programming, which tries to transform an input program to be protected against side-channel attacks such that an operation working on a data element/block to be protected would execute in an amount of time that is independent of the input. Unfortunately, while quite effective from a security angle, constant-time programming can lead to severe performance penalties.

To further improve the convenience and effectiveness of human computer interaction (HCI) with smart devices, human activity recognition (HAR) has been widely studied from various aspects. Unfortunately, deep learning based methods often suffer from either expensive labeling efforts or weak generalization ability. Inspired by recently developed domain adaptation strategies, we propose XHAR, a novel adversarial deep domain adaptation framework for HAR using smart devices, providing better device and user adaptation. XHAR first selects the most similar source dataset (with label), then extracts device and user independent spatial-temporal features through the combinations of Convolutional Neural Networks (CNN) and Bidirectional Gated Recurrent Units (BiGRU) feature extractors. Moreover, it removes the distribution discrepancy using multiple domain discriminators, and finally performs adaptation on the target dataset (without label) to obtain the predicted labels. We conduct extensive experiments on 50 users (i.e., of different ages, genders, and body shapes) and 4 smart devices with two kinds of datasets (i.e., gesture activities and sport activities). We compare our method with the source-only model and several state-of-the-art domain adaptation models. The results show that XHAR increases the classification accuracy by at least 4.81% (to 74.50%) on the adaptation between different users, and accordingly by at least 9.25% (to 69.23%) between different devices.