Image classifiers often rely on convolutional neural networks (CNN) for their tasks, which, for image classification, experience high latency due to the number of operations they perform, 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 image classification approach using a vectorized view of images. We exploit the lightweightness of MLPs by viewing images as vectors 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，但在图像分类中，由于CNN执行大量运算导致高延迟，这在实时应用中可能存在问题。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少见。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和合成孔径雷达（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图像分类方法。通过将图像视为向量并将问题简化为灰度图像分类场景，我们利用了多层感知机（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上开发了定制化加速器，并通过多项优化提升其性能。在基准灰度图像数据集上的实验结果表明，所提模型具有显著优势：与各领域专用灰度图像分类数据集上的其他先进图像分类模型相比，本模型在保持竞争力或领先性能的同时，实现了大幅降低的延迟（最高可达16$\times$）。