Hardware Feasibility Analysis of Spatial Propagation Algorithms
Journal
Proceedings - 2020 IEEE International Conference on Artificial Intelligence Circuits and Systems, AICAS 2020
Pages
324
Date Issued
2020
Author(s)
Lin, C.-T.
Abstract
Although convolutional neural networks (CNNs) have revolutionized many computer vision tasks in recent years, spatial propagation algorithm still plays an important role in many applications such as image matting, segmentation, depth estimation, and colorization. However, most of these methods focus on qualitative performance and result in expensive computation, which is not conducive to be deployed on mobile or edge devices. In this paper, we take the spatial propagation network (SPN), convolutional spatial propagation network (CSPN), and guided convolution as our analysis objects. We evaluate these methods from the hardware accelerating aspect and use depth completion as an example. In addition to quality comparison, we propose a hardware feasibility metric that analyzes each method in terms of hardware resources. With this metric, developers can easily decide a proper algorithm on a specific hardware platform. We apply it to an example and get a consistent result, successfully building the connection between algorithm and hardware design. © 2020 IEEE.
SDGs
Other Subjects
Backpropagation; Convolution; Convolutional neural networks; Image segmentation; Depth Estimation; Feasibility analysis; Hardware design; Hardware resources; Image matting; Spatial propagation; Specific hardware; Computer hardware
Type
conference paper