Performance characterization and optimization of mobile augmented reality on handheld platforms

The introduction of low power general purpose processors (like the Intel® Atom™ processor) expands the capability of handheld and mobile internet devices (MIDs) to include compelling visual computing applications. One rapidly emerging visual computing usage model is known as mobile augmented reality (MAR). In the MAR usage model, the user is able to point the handheld camera to an object (like a wine bottle) or a set of objects (like an outdoor scene of buildings or monuments) and the device automatically recognizes and displays information regarding the object(s). Achieving this on the handheld requires significant compute processing resulting in a response time in the order of several seconds. In this paper, we analyze a MAR workload and identify the primary hotspot functions that incur a large fraction of the overall response time. We also present a detailed architectural characterization of the hotspot functions in terms of CPI, MPI, etc. We then implement and analyze the benefits of several software optimizations: (a) vectorization, (b) multi-threading, (c) cache conflict avoidance and (d) miscellaneous code optimizations that reduce the number of computations. We show that a 3X performance improvement in execution time can be achieved by implementing these optimizations. Overall, we believe our analysis provides a detailed understanding of the processing for a new domain of visual computing workloads (i.e. MAR) running on low power handheld compute platforms.