This paper takes an important step towards the improvement of the AR mobile experience by designing and developing ARBench, the first Augmented Reality (AR) benchmark for mobile devices. ARBench incorporates different AR workloads that stress multiple hardware units of the SoC (CPU, GPU, DSP, etc), and measures the individual score for each AR workload. The proposed benchmark suite is then used to evaluate the AR performance of various commercial mobile devices, and their ability to support various functions of AR workloads.

The need for faster Systems on Chip (SoCs) has accelerated scaling trends, leading to a considerable power density increase and raising critical power and thermal challenges. The ability to measure power consumption of different hardware units is essential for the operation and improvement of mobile SoCs, as well as the enhancement of the power efficiency of the software that runs on them. SoCs are usually enabled with embedded thermal sensors to measure the temperature at the hardware unit level; however, they lack the ability to sense the power. In this paper we introduce an Alternating Blind Identification of Power sources (Alternating-BPI), a technique that accurately estimates the power consumption of individual SoC units without the use of any design based models. The proposed technique uses a novel approach to blindly identify the sources of power consumption, by relying only on the measurements from the embedded thermal sensors and the total power consumption. The accuracy and applicability of the proposed technique was verified using simulation and experimental data. Alternating-BPI is able to estimate the power at the SoC hardware unit level with up to 98.1% accuracy. Furthermore, we demonstrate the applicability of the proposed technique on a commercial SoC and provide a fine-grain analysis of the power profiles of CPU and GPU Apps, as well as Artificial Intelligence (AI), Virtual Reality (VR) and Augmented Reality (AR) Apps. Additionally, we demonstrate that the proposed technique could be used to estimate the power consumption per-process by relying on the estimated per-unit power numbers and per-unit hardware utilization numbers. The analysis provided by the proposed technique gives useful insights about the power efficiency of the different hardware units on a state-of-the-art commercial SoC.

Abstract Recent advances in computer graphics have relied on high‐quality textures in order to generate photorealistic real‐time images. Texture compression standards meet these growing demands for data, but current texture compression schemes use fixed‐rate methods where statically sized blocks of pixels are represented using the same numbers of bits irrespective of their data content. In order to account for the natural variation in detail, we present an alternative format that allows variable bit‐rate texture compression with minimal changes to texturing hardware. Our proposed scheme uses one additional level of indirection to allow the variation of the block size across the same texture. This single change is exploited to both vary the amount of bits allocated to certain parts of the texture and to duplicate redundant texture information across multiple pixels. To minimize hardware changes, the method picks combinations of block sizes and compression methods from existing fixed‐rate standards. With this approach, our method is able to demonstrate energy savings of up to 50%, as well as higher quality compressed textures over current state of the art techniques.

We present an interactive wave-based sound propagation system that generates accurate, realistic sound in virtual environments for dynamic (moving) sources and listeners. We propose a novel algorithm to accurately solve the wave equation for dynamic sources and listeners using a combination of precomputation techniques and GPU-based runtime evaluation. Our system can handle large environments typically used in VR applications, compute spatial sound corresponding to listener's motion (including head tracking) and handle both omnidirectional and directional sources, all at interactive rates. As compared to prior wave-based techniques applied to large scenes with moving sources, we observe significant improvement in runtime memory. The overall sound-propagation and rendering system has been integrated with the Half-Life 2 game engine, Oculus-Rift head-mounted display, and the Xbox game controller to enable users to experience high-quality acoustic effects (e.g., amplification, diffraction low-passing, high-order scattering) and spatial audio, based on their interactions in the VR application. We provide the results of preliminary user evaluations, conducted to study the impact of wave-based acoustic effects and spatial audio on users' navigation performance in virtual environments.

Local collision avoidance algorithms in crowd simulation often ignore agents beyond a neighborhood of a certain size. This cutoff can result in sharp changes in trajectory when large groups of agents enter or exit these neighborhoods. In this work, we exploit the insight that exact collision avoidance is not necessary between agents at such large distances, and propose a novel algorithm for extending existing collision avoidance algorithms to perform approximate, long-range collision avoidance. Our formulation performs long-range collision avoidance for distant agent groups to efficiently compute trajectories that are smoother than those obtained with state-of-the-art techniques and at faster rates. Comparison to real-world data demonstrates that crowds simulated with our algorithm exhibit an improved speed sensitivity to density similar to human crowds. Another issue often sidestepped in existing work is that discrete and continuum collision avoidance algorithms have different regions of applicability. For example, low-density crowds cannot be modeled as a continuum, while high-density crowds can be expensive to model using discrete methods. We formulate a hybrid technique for crowd simulation which can accurately and efficiently simulate crowds at any density with seamless transitions between continuum and discrete representations. Our approach blends results from continuum and discrete algorithms, based on local density and velocity variance. In addition to being robust across a variety of group scenarios, it is also highly efficient, running at interactive rates for thousands of agents on portable systems.

Local collision avoidance algorithms in crowd simulation often ignore agents beyond a neighborhood of a certain size. This cutoff can result in sharp changes in trajectory when large groups of agents enter or exit these neighborhoods. In this work, we exploit the insight that exact collision avoidance is not necessary between agents at such large distances, and propose a novel algorithm for extending existing collision avoidance algorithms to perform approximate, long-range collision avoidance. Our formulation performs long-range collision avoidance for distant agent groups to efficiently compute trajectories that are smoother than those obtained with state-of-the-art techniques and at faster rates.

Simulating fluids in large-scale scenes with appreciable quality using state-of-the-art methods can lead to high memory and compute requirements. Since memory requirements are proportional to the product of domain dimensions, simulation performance is limited by memory access, as solvers for elliptic problems are not compute-bound on modern systems. This is a significant concern for large-scale scenes. To reduce the memory footprint and memory/compute ratio, vortex singularity bases can be used. Though they form a compact bases for incompressible vector fields, robust and efficient modeling of nonrigid obstacles and free-surfaces can be challenging with these methods. We propose a hybrid domain decomposition approach that couples Eulerian velocity-based simulations with vortex singularity simulations. Our formulation reduces memory footprint by using smaller Eulerian domains with compact vortex bases, thereby improving the memory/compute ratio, and simulation performance by more than 1000x for single phase flows as well as significant improvements for free-surface scenes. Coupling these two heterogeneous methods also affords flexibility in using the most appropriate method for modeling different scene features, as well as allowing robust interaction of vortex methods with free-surfaces and nonrigid obstacles.

We present a novel continuum-based model that enables efficient simulation of granular materials. Our approach fully solves the internal pressure and frictional stresses in a granular material, thereby allows visually noticeable behaviors of granular materials to be reproduced, including freely dispersing splashes without cohesion, and a global coupling between friction and pressure. The full treatment of internal forces in the material also enables two-way interaction with solid bodies. Our method achieves these results at only a very small fraction of computational costs of the comparable particle-based models for granular flows.

Large dense crowds show aggregate behavior with reduced individual freedom of movement. We present a novel, scalable approach for simulating such crowds, using a dual representation both as discrete agents and as a single continuous system. In the continuous setting, we introduce a novel variational constraint calledunilateral incompressibility, to model the large-scale behavior of the crowd, and accelerate inter-agent collision avoidance in dense scenarios. This approach makes it possible to simulate very large, dense crowds composed of up to a hundred thousand agents at near-interactive rates on desktop computers.