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With the advent of low-power ultra-fast hardware and GPUs, virtual reality(VR) has gained a lot of prominence in the last few years and is being used invarious areas such as education, entertainment, scientific visualization, andcomputer-aided design. VR-based applications are highly interactive, and one ofthe most important performance metrics for these applications is themotion-to-photon-delay (MPD). MPD is the delay from the users head movement tothe time at which the image gets updated on the VR screen. Since the humanvisual system can even detect an error of a few pixels (very spatiallysensitive), the MPD should be as small as possible. Popular VR vendors use theGPU-accelerated Asynchronous Time Warp (ATW) algorithm to reduce the MPD. ATWreduces the MPD if and only if the warping operation finishes just before thedisplay refreshes. However, due to the competition between applications for theshared GPU, the GPU-accelerated ATW algorithm suffers from an unpredictable ATWlatency, making it challenging to find the ideal time instance for starting thetime warp and ensuring that it completes with the least amount of lag relativeto the screen refresh. Hence, the state-of-the-art is to use a separatehardware unit for the time warping operation. Our approach, PredATW, uses anML-based predictor to predict the ATW latency for a VR application, and thenschedule it as late as possible. This is the first work to do so. Our predictorachieves an error of 0.77 ms across several popular VR applications forpredicting the ATW latency. As compared to the baseline architecture, we reducedeadline misses by 73.1%.

Minimizing the Motion-to-Photon-delay (MPD) in Virtual Reality Systems