A new generation of augmented reality (AR) devices, such as
\nthe Microsoft HoloLens, promises a user experience known
\nas mixed reality (MR) that is more seamless, immersive, and
\nintelligent than earlier AR technologies. However, this new
\nexperience comes with high computational costs, including
\nexceptionally low latency and high quality requirements.
\nWhile this cost could be offset in part through offloading,
\nwe also observe an increasing availability of on-device, task-
\nspecific accelerators. In this paper, we propose collaborative
\nacceleration, a collaborative technique that utilizes the unique
\nhardware accelerated capabilities of an MR device, in con-
\njunction with an edge node, to partition an application\u2019s
\ncore workflow according to the specific strengths of each
\ndevice. To better understand the workloads of next gener-
\nation MR applications, we implement a concrete MR app
\non the HoloLens: an assistive tool to visually aid users in
\nmanipulating physical objects. Through our prototype, we
\nfind that offloading a subset of the app\u2019s workload to an edge
\nwhile also leveraging the strengths of the HoloLens delivers
\naccurate enough results at a low latency. Our work provides
\nan early glimpse into the system design challenges of MR,
\npotentially the first \u201ckiller application\u201d of edge offloading.<\/p>\n","protected":false},"excerpt":{"rendered":"
A new generation of augmented reality (AR) devices, such as the Microsoft HoloLens, promises a user experience known as mixed reality (MR) that is more seamless, immersive, and intelligent than earlier AR technologies. However, this new experience comes with high computational costs, including exceptionally low latency and high quality requirements. While this cost could be 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