Convolutional Neural Networks (CNN) continue to dominate research in the area of hardware acceleration using Field Programmable Gate Arrays (FPGA), proving its effectiveness in a variety of computer vision applications such as object segmentation, image classification, face detection, and traffic signs recognition, among others. However, there are numerous constraints for deploying CNNs on FPGA, including limited on-chip memory, CNN size, and configuration parameters. This paper introduces Ad-MobileNet, an advanced CNN model inspired by the baseline MobileNet model. The proposed model uses an Ad-depth engine, which is an improved version of the depth-wise separable convolution unit. Moreover, we propose an FPGA-based implementation model that supports the Mish, TanhExp, and ReLU activation functions. The experimental results using the CIFAR-10 dataset show that our Ad-MobileNet has a classification accuracy of 88.76% while requiring little computational hardware resources. Compared to state-of-the-art methods, our proposed method has a fairly high recognition rate while using fewer computational hardware resources. Indeed, the proposed model helps to reduce hardware resources by more than 41% compared to that of the baseline model.

中文翻译：

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用于分类的高效基于 FPGA 的卷积神经网络：Ad-MobileNet

卷积神经网络 (CNN) 继续主导使用现场可编程门阵列 (FPGA) 的硬件加速领域的研究，证明其在各种计算机视觉应用中的有效性，例如对象分割、图像分类、人脸检测和交通标志识别，等等。然而，在 FPGA 上部署 CNN 有很多限制，包括有限的片上内存、CNN 大小和配置参数。本文介绍了 Ad-MobileNet，这是一种受基线 MobileNet 模型启发的高级 CNN 模型。所提出的模型使用 Ad-depth 引擎，它是深度可分离卷积单元的改进版本。此外，我们提出了一个基于 FPGA 的实现模型，支持 Mish、TanhExp 和 ReLU 激活函数。使用 CIFAR-10 数据集的实验结果表明，我们的 Ad-MobileNet 具有 88.76% 的分类准确率，同时需要很少的计算硬件资源。与最先进的方法相比，我们提出的方法具有相当高的识别率，同时使用更少的计算硬件资源。实际上，与基线模型相比，所提出的模型有助于将硬件资源减少 41% 以上。