Parallelizing computer vision algorithms on acceleration technologies: A SIFT case study

Computer vision algorithms, such as scale-invariant feature transform (SIFT), are used in many important applications, e.g., autonomous vehicle and computer-human interaction. They are typically computation intensive and require a long processing time on traditional single-core processors. In this work, we present the methodologies to parallelize the SIFT algorithm on various acceleration technologies and multicore processors, such as field-programmable gate arrays (FPGAs), graphics processing units (GPUs), and Intel Many Integrated Core (MIC) Architecture. The results show that all acceleration technologies can significantly improve the performance compared with the single-thread implementation on an Intel Core i7 processor. In particular the Nvidia Telsa K20 GPU is capable of providing a 10× speedup. Furthermore, it is found that the performance of the Intel MIC coprocessor is at the same range as the multicore CPU processor, while the optimal implementation of SIFT does not necessarily use all the computing resources on these two platforms.