Heterogeneous hardware accelerator architecture for streaming image processing

This paper proposes a heterogeneous hardware accelerator architecture to support streaming image processing. Each image in a data-set is pre-processed on a host processor and sent to hardware kernels. The host processor and the hardware kernels process a stream of images in parallel. The Convey hybrid computing system is used to develop our proposed architecture. We use the Canny edge detection algorithm as our case study. The data-set used for our experiment contains 7200 images. Experimental results show that the system with the proposed architecture achieved a speed-up of the kernels by 2.13× and of the whole application by 2.40× with respect to a software implementation running on the host processor. Moreover, our proposed system achieves 55% energy reduction compared to a hardware accelerator system without streaming support.