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.

翻译：图像分类器通常依赖卷积神经网络（CNN）执行任务，但这类网络本质上比多层感知机（MLP）更为复杂，在实时应用中可能引发问题。此外，许多图像分类模型可同时处理RGB和灰度数据集，而专门针对灰度图像设计的分类器则较为罕见。灰度图像分类具有广泛的应用场景，包括但不限于医学图像分类与合成孔径雷达（SAR）自动目标识别（ATR）。为此，我们提出一种基于图像向量化视角的新型灰度（单通道）图像分类方法。通过将图像视为向量并将问题设定简化为灰度图像分类场景，我们充分利用了MLP的轻量化特性。研究发现，在批量处理中使用单层图卷积能有效提升模型精度并降低性能方差。此外，我们为所提模型在FPGA上设计了定制化加速器，通过多项优化策略提升其性能。在基准灰度图像数据集上的实验结果表明，该模型具有显著优势：与当前最先进的图像分类模型相比，在多个特定领域的灰度图像分类数据集上实现了大幅降低的延迟（最高达16倍）以及具有竞争力或领先的性能表现。