Abstract-Mobile Augmented Reality (MAR) is an emerging visual computing application for the mobile internet device (MID). In one MAR usage model, the user points the handheld device to an object (like a wine bottle or a building) and the MID automatically recognizes and displays information regarding the object. Achieving this in software on the handheld requires significant compute processing for object recognition and matching. In this paper, we identify hotspot functions of the MAR workload on a low-power x86 platform that motivates acceleration. We present the detailed design of two hardware accelerators, one for object recognition (MAR-HA) and the other for match processing (MAR-MA). We also quantify the performance and area efficiency of the hardware accelerators. Our analysis shows that hardware acceleration has the potential to improve the individual hotspot functions by as much as 20x, and overall response time by 7x. As a result, user response time can be reduced significantly.

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Almost all hardware platforms to date have been homogeneous with one or more identical processors managed by the operating system (OS). However, recently, it has been recognized that power constraints and the need for domain-specific high performance computing may lead architects towards building heterogeneous architectures and platforms in the near future. In this paper, we consider the three types of heterogeneous core architectures: (a) Virtual asymmetric cores: multiple processors that have identical core micro-architectures and ISA but each running at a different frequency point or perhaps having a different cache size, (b) Physically asymmetric cores: heterogeneous cores, each with a fundamentally different microarchitecture (in-order vs. out-of-order for instance) running at similar or different frequencies, with identical ISA and (c) Hybrid cores: multiple cores, where some cores have tightly-coupled hardware accelerators or special functional units. We show case studies that highlight why existing OS and hardware interaction in such heterogeneous architectures is inefficient and causes loss in application performance, throughput efficiency and lack of quality of service. We then discuss hardware and software support needed to address these challenges in heterogeneous platforms and establish efficient heterogeneous environments for platforms in the next decade. In particular, we will outline a monitoring and prediction framework for heterogeneity along with software support to take advantage of this information. Based on measurements on real platforms, we will show that these proposed techniques can provide significant advantage in terms of performance and power efficiency in heterogeneous platforms.

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