Hardware-Centric Vision Processing for Mobile IoT Environment Exploiting Approximate Graph cut in Resistor Grid

Abstract

The Internet of things (IoT) has become a general trend in the electronic world. In this IoT era, various types of sensors gathering data are placed everywhere, and visual data is one of the most valuable information. While extracting important features from visual images, complex vision algorithms are often processed by high speed computing units like CPU or GPU. However, most IoT devices are mobile, so hardware resources are severely limited, which disturbs usage of the high performance processors in IoT appliances. Therefore, efficient vision computing becomes a crucial concern in this resource poor environment. Graph cut is a popular and widespread algorithm for image segmentation, image denoising, and stereo matching. However, it suffers from inefficient memory access patterns and massive digital computations. Due to the small hardware capacity not enough to overcome these obstacles, it is hardly feasible to run the algorithm in the IoT devices. In this paper, we propose an approximate version of the graph cut with the aid of the dedicated hardware design. By exploiting the analogy between the max flow and electric currents, we design the theoretical model of an approximate max flow solution and suggest the resistor grid as its processing circuit. Because this work utilizes electric potentials for the min cut computation, it is possible to find the cut at a low energy and a high speed. A prototype circuit is designed and simulated for evaluation. Using public GrabCut benchmark, algorithm evaluations are performed. As a result, we can verify a fast and an energy-efficient image processing with an acceptable accuracy. For supervised binary image segmentation, speed efficiency is 4.57 times higher, and energy-efficiency is 9.11 times better when compared to other image/video segmentation works.