Graphics processing units (GPUs) are capable of achieving remarkable performance improvements for a broad range of applications. However, they have not been widely adopted in embedded systems and mobile devices as accelerators mainly due to their relatively higher power consumption compared with embedded microprocessors. In this work, we conduct a comprehensive analysis regarding the feasibility and potential of accelerating applications using GPUs in low-power domains. We use two different categories of benchmarks: (1) the Level 3 BLAS subroutines, and (2) the computer vision algorithms, i.e., mean shift image segmentation and scale-invariant feature transform (SIFT). We carried out our experiments on the Nvidia CARMA development kit, which consists of a Nvidia Tegra 3 quad-core CPU and a Nvidia Quadro 1000M GPU. It is found that the GPU can deliver a remarkable performance speedup compared with the CPU while using a significantly less energy for most benchmarks. Further we propose a hybrid approach to developing applications on platform with GPU accelerators. This approach optimally distributes workload between the parallel GPU and the sequential CPU to achieve the best performance while using the least energy.

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Accelerating Applications Using GPUs on Embedded Systems and Mobile Devices

Provided are a method for performing out focus using depth information and a camera using the same. The present method of performing out focus may include photographing a first image with a first lens of a same subject, photographing a second image with a second lens, photographing a first image by using the photographed first image and the second image, and Extracting depth information of the second image. The focus is performed on the first image or the second image by using the extracted depth information. Accordingly, the user can implement the same effect at a lower cost than the medium-large digital camera (DSLR) by using a conventional small digital camera such as a DSC or a phone camera.

Method for performing out-focus using depth information and camera using the same

Bokeh application presents the blur or the aesthetic quality of blurring in out-of-focus areas of an image. The out-of-focus effect of Bokeh results depends on accuracy of depth information and blurring effects produced by image postprocessing. To obtain accurate depth information, current stereo vision techniques however consume a huge amount of processing time. In this paper, we present a case study on parallelizing a Bokeh application on an embedded multicore platform, which features one MPU and one DSP sub-system consisting of two VLIW DSP processors. The Bokeh application employs a Belief Propagation method to obtain depth information of input images and uses the information to generate output images with out-of-focus effect. This study also illustrates how to deliver performance for applications on embedded multicore systems. To sustain heavy computation requirement of the stereo vision techniques, DSPs with their SIMD instructions are leveraged to exploit data parallelism in critical kernels. In addition, DMAs on the multicore system are also incorporated to facilitate data transmission between processors. The access to SIMD and DMAs is provided by two essential programming models we developed for embedded multicore systems. Our work also gives the firsthand experiences of how C++ classes and abstractions can be used to help parallelization of applications on embedded multicore DSP systems. Finally, in our experiments, we utilize DSPs, SIMD and DMAs to obtain performance for two key components of the Bokeh application with their speedups of 1.67 and 2.75, respectively.

Kun-Hsien Tsai

Papers

Parallelization of a Bokeh application on embedded multicore DSP systems