基于Winograd算法的卷积神经网络 硬件加速研究

摘要：卷积神经网络（CNN）在图像识别、语音识别、自然语言处理等领域取得了重要进展，但其计算量巨大，限制了其在嵌入式设备等资源有限的场景中的应用。Winograd算法是一种高效的卷积计算算法，已经被广泛应用于CPU和GPU的优化中。本文在此基础上，研究了基于Winograd算法的CNN硬件加速方法。首先介绍了Winograd算法的原理和优势，然后提出了基于Winograd算法的卷积神经网络硬件加速器的架构和实现方法，并对其进行了性能测试和分析。实验结果表明，基于Winograd算法的CNN硬件加速器相比于传统的卷积计算方法，在计算速度和功耗上都有显著的提升，能够更好地满足嵌入式设备等资源有限场景下的应用需求。

关键词：卷积神经网络；Winograd算法；硬件加速；嵌入式设备

Abstract: Convolutional neural networks (CNNs) have made significant progress in fields such as image recognition, speech recognition, and natural language processing, but their huge computational complexity limits their application in resource-limited scenarios such as embedded devices. The Winograd algorithm is an efficient convolutional calculation algorithm that has been widely used in CPU and GPU optimization. Based on this, this paper studies the hardware acceleration method of CNN based on Winograd algorithm. First, the principle and advantages of the Winograd algorithm are introduced. Then, the architecture and implementation method of the CNN hardware accelerator based on the Winograd algorithm are proposed, and its performance is tested and analyzed. The experimental results show that the CNN hardware accelerator based on the Winograd algorithm has significant improvements in calculation speed and power consumption compared with traditional convolutional calculation methods, which can better meet the application requirements in resource-limited scenarios such as embedded devices.

Keywords: Convolutional neural network; Winograd algorithm; Hardware acceleration; Embedded devices.