AFFIX - Automatic Acceleration Framework for FPGA Implementation of OpenVX Vision Algorithms.

Computer vision algorithms are computationally expensive and difficult to implement efficiently. Field Programmable Gate Arrays (FPGA)s offer a promising direction to reduce the computation cost by exploiting hardware parallelism. However, it is difficult to translate vision algorithms to FPGA bitstream efficiently. OpenVX is an industry standard for graph-based representation of vision algorithms. It defines a set of widely used vision kernels and data structures that can be used to form a Directed Acyclic Graph (DAG) to represent a vision algorithm. This paper proposes a framework for automatic FPGA acceleration of computer vision algorithms based on OpenVX specification, called AFFIX. AFFIX receives a vision algorithm formed using the OpenVX and generates a heterogeneous CPU-FPGA implementation. AFFIX incorporates several high level and low-level optimization methods to improve the efficiency of the FPGA implementation. It provides a configurable and extensible framework that enables vision algorithm developers to quickly develop, verify and test FPGA implementations of vision algorithms. We demonstrate the effectiveness of the proposed framework via development and evaluations of multiple vision algorithms.