A Single Graph Convolution Is All You Need: Efficient Grayscale Image
  Classification
Jacob Fein-Ashley Tian Ye Sachini Wickramasinghe Bingyi Zhang Rajgopal Kannan Viktor Prasanna
Image classifiers often rely on convolutional neural networks (CNN) for their
tasks, which are inherently more heavyweight than multilayer perceptrons
(MLPs), which can be problematic in real-time applications. Additionally, many
image classification models work on both RGB and grayscale datasets.
Classifiers that operate solely on grayscale images are much less common.
Grayscale image classification has diverse applications, including but not
limited to medical image classification and synthetic aperture radar (SAR)
automatic target recognition (ATR). Thus, we present a novel grayscale (single
channel) image classification approach using a vectorized view of images. We
exploit the lightweightness of MLPs by viewing images as a vector and reducing
our problem setting to the grayscale image classification setting. We find that
using a single graph convolutional layer batch-wise increases accuracy and
reduces variance in the performance of our model. Moreover, we develop a
customized accelerator on FPGA for the proposed model with several
optimizations to improve its performance. Our experimental results on benchmark
grayscale image datasets demonstrate the effectiveness of the proposed model,
achieving vastly lower latency (up to 16$\times$ less) and competitive or
leading performance compared to other state-of-the-art image classification
models on various domain-specific grayscale image classification datasets.