TY - GEN
T1 - HoloAR
T2 - 54th Annual IEEE/ACM International Symposium on Microarchitecture, MICRO 2021
AU - Zhao, Shulin
AU - Zhang, Haibo
AU - Mishra, Cyan S.
AU - Bhuyan, Sandeepa
AU - Ying, Ziyu
AU - Kandemir, Mahmut T.
AU - Sivasubramaniam, Anand
AU - Das, Chita R.
N1 - Funding Information:
This research is supported in part by NSF grants #1763681, #1629915, #1629129, #1317560, #1526750, #1714389, #1912495, and #1909004. This work was also supported in part by CRISP, one of six centers in JUMP, a Semiconductor Research Corporation (SRC) program sponsored by DARPA. We would also like to thank Dr. Jack Sampson and Dr. Dinghao Wu for their feedback on this paper.
Publisher Copyright:
© 2021 Association for Computing Machinery.
PY - 2021/10/18
Y1 - 2021/10/18
N2 - Hologram processing is the primary bottleneck and contributes to more than 50% of energy consumption in battery-operated augmented reality (AR) headsets. Thus, improving the computational efficiency of the holographic pipeline is critical. The objective of this paper is to maximize its energy efficiency without jeopardizing the hologram quality for AR applications. Towards this, we take the approach of analyzing the workloads to identify approximation opportunities. We show that, by considering various parameters like region of interest and depth of view, we can approximate the rendering of the virtual object to minimize the amount of computation without affecting the user experience. Furthermore, by optimizing the software design flow, we propose HoloAR, which intelligently renders the most important object in sight to the clearest detail, while approximating the computations for the others, thereby significantly reducing the amount of computation, saving energy, and gaining performance at the same time.We implement our design in an edge GPU platform to demonstrate the real-world applicability of our research. Our experimental results show that, compared to the baseline, HoloAR achieves, on average, 2.7× speedup and 73% energy savings.
AB - Hologram processing is the primary bottleneck and contributes to more than 50% of energy consumption in battery-operated augmented reality (AR) headsets. Thus, improving the computational efficiency of the holographic pipeline is critical. The objective of this paper is to maximize its energy efficiency without jeopardizing the hologram quality for AR applications. Towards this, we take the approach of analyzing the workloads to identify approximation opportunities. We show that, by considering various parameters like region of interest and depth of view, we can approximate the rendering of the virtual object to minimize the amount of computation without affecting the user experience. Furthermore, by optimizing the software design flow, we propose HoloAR, which intelligently renders the most important object in sight to the clearest detail, while approximating the computations for the others, thereby significantly reducing the amount of computation, saving energy, and gaining performance at the same time.We implement our design in an edge GPU platform to demonstrate the real-world applicability of our research. Our experimental results show that, compared to the baseline, HoloAR achieves, on average, 2.7× speedup and 73% energy savings.
UR - http://www.scopus.com/inward/record.url?scp=85118900698&partnerID=8YFLogxK
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U2 - 10.1145/3466752.3480056
DO - 10.1145/3466752.3480056
M3 - Conference contribution
AN - SCOPUS:85118900698
T3 - Proceedings of the Annual International Symposium on Microarchitecture, MICRO
SP - 494
EP - 506
BT - MICRO 2021 - 54th Annual IEEE/ACM International Symposium on Microarchitecture, Proceedings
PB - IEEE Computer Society
Y2 - 18 October 2021 through 22 October 2021
ER -