Computer Architectures for Mobile Computer Vision Systems.

Mobile;Computer Architecture;Computer Vision;Computer Science;Engineering;Computer Science & Engineering

Clemons, Jason LavarMahlke, Scott ;

University of Michigan

关键词: Mobile;    Computer Architecture;    Computer Vision;    Computer Science;    Engineering;    Computer Science & Engineering;   

Others  :  https://deepblue.lib.umich.edu/bitstream/handle/2027.42/97782/jclemons_1.pdf?sequence=1&isAllowed=y

瑞士|英语

来源: The Illinois Digital Environment for Access to Learning and Scholarship

【 摘 要 】

Mobile vision is enabling many new applications such as face recognition and augmented reality. However, the performance of mobile processors is limiting the capability of mobile vision computing.This dissertation presents an in-depth analysis of mobile computer vision applications and proposes novel hardware and software optimizations with the goal to increase mobile computer vision processing capability. We present the Michigan Visual Sonification System, a new mobile vision application that provides navigational aid to the visually impaired.The development of this application gives insights into the nature of mobile vision applications including the tradeoffs between performance and energy on mobile processors.We then present MEVBench, a mobile vision benchmark suite that we built to determine the computational characteristics of various mobile vision kernels.This analysis exposes the vector reduction operations, the imbalanced task or thread parallelism and the 2D spatial locality in memory accesses, all which we exploit in the pursuit of highly efficient mobile vision architectures.Armed with a deeper understanding of computer vision processing, the core of this thesis focuses on software and hardware based optimization to improve the efficiency of mobile vision processing.We begin the optimization with a software optimization known as Single Eigenvector Solver (SEVS), an algorithm that reduces the computation for augmented reality applications.We begin the hardware optimizations with EFFEX, a heterogeneous multicore architecture that utilizes vector reduction functional units and a 2D memory controller to improve the efficiency of feature extraction.We close with the Efficient Vision Architecture (EVA).EVA expands the EFFEX architecture by adding more custom accelerators for vision operations beyond feature extraction.It also utilizes the tile cache to allow for both 1D and 2D spatial locality in cache accesses.Overall, this dissertation demonstrates that an application specific approach to processor design can create a flexible programmable design with significant efficiency improvements in mobile vision performance when compared to currently available mobile processors.These works enable the development of richer more capable mobile vision systems.