Paper Info
Title
AFFIX - Automatic Acceleration Framework for FPGA Implementation of OpenVX Vision Algorithms.
Abstract
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.
Year
DOI
Venue
2019
10.1145/3289602.3293907
FPGA
Keywords
Field
DocType
Acceleration, Computer vision, FPGA, OpenVX, OpenCL
Data structure,Computer science,Parallel computing,Image processing,Field-programmable gate array,Directed acyclic graph,Acceleration,Bitstream,Computer engineering,Extensibility,Computation
Conference
ISBN
Citations 
PageRank 
978-1-4503-6137-8
2
0.38
References 
Authors
12
5
Name
Order
Citations
PageRank
Sajjad Taheri182.86
Payman Behnam2357.49
Elaheh Bozorgzadeh363037.93
Alexander V. Veidenbaum475778.24
Alexandru Nicolau52265307.74