Computer Vision Accelerators for Mobile Systems based on OpenCL GPGPU Co-Processing
This work addresses the problem of computational efficiency for computer vision applications on mobile devices, representing an incremental improvement through optimization of existing methods.
The paper tackled the challenge of running computer vision algorithms efficiently on mobile devices by implementing an image inpainting-based object removal algorithm using OpenCL-based heterogeneous computing with CPU-GPU co-processing, resulting in significant speed-ups while maintaining output quality.
In this paper, we present an OpenCL-based heterogeneous implementation of a computer vision algorithm -- image inpainting-based object removal algorithm -- on mobile devices. To take advantage of the computation power of the mobile processor, the algorithm workflow is partitioned between the CPU and the GPU based on the profiling results on mobile devices, so that the computationally-intensive kernels are accelerated by the mobile GPGPU (general-purpose computing using graphics processing units). By exploring the implementation trade-offs and utilizing the proposed optimization strategies at different levels including algorithm optimization, parallelism optimization, and memory access optimization, we significantly speed up the algorithm with the CPU-GPU heterogeneous implementation, while preserving the quality of the output images. Experimental results show that heterogeneous computing based on GPGPU co-processing can significantly speed up the computer vision algorithms and makes them practical on real-world mobile devices.