Efficiently mapping high-performance early vision algorithms onto multicore embedded platforms

Computer vision;Embedded;Multicore

Apewokin, Senyo ; Electrical and Computer Engineering

University:Georgia Institute of Technology

Department:Electrical and Computer Engineering

关键词: Computer vision;    Embedded;    Multicore;   

Others  :  https://smartech.gatech.edu/bitstream/1853/28256/1/apewokin_senyo_b_200812_phd.pdf

美国|英语

来源: SMARTech Repository

【 摘 要 】

The combination of low-cost imaging chips and high-performance, multicore, embedded processors heralds a new era in portable vision systems. Early vision algorithms have the potential for highly data-parallel, integer execution. However, an implementation must operate within the constraints of embedded systems including low clock rate, low-power operation and with limited memory. This dissertation explores new approaches to adapt novel pixel-based vision algorithms for tomorrow's multicore embedded processors. It presents :- An adaptive, multimodal background modeling technique called Multimodal Mean that achieves high accuracy and frame rate performance with limited memory and a slow-clock, energy-efficient, integer processing core.- A new workload partitioning technique to optimize the execution of early vision algorithms on multi-core systems.- A novel data transfer technique called cat-tail dma that provides globally-ordered, non-blocking data transfers on a multicore system.By using efficient data representations, Multimodal Mean provides comparable accuracy to the widely used Mixture of Gaussians (MoG) multimodal method. However, it achieves a 6.2x improvement in performance while using 18% less storage than MoG while executing on a representative embedded platform. When this algorithm is adapted to a multicore execution environment, the new workload partitioning technique demonstrates an improvement in execution times of 25% with only a 125 ms system reaction time. It also reduced the overall number of data transfers by 50%.Finally, the cat-tail buffering technique reduces the data-transfer latency between execution cores and main memory by 32.8% over the baseline technique when executing Multimodal Mean. This technique concurrently performs data transfers with code execution on individual cores, while maintaining global ordering through low-overhead scheduling to prevent collisions.