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Convolutional neural network acceleration with hardware/software co-design

机译:卷积神经网络加速硬件/软件共同设计

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摘要

Convolutional Neural Networks (CNNs) have a broad range of applications, such as image processing and natural language processing. Inspired by the mammalian visual cortex, CNNs have been shown to achieve impressive results on a number of computer vision challenges, but often with large amounts of processing power and no timing restrictions. This paper presents a design methodology for accelerating CNNs using Hardware/Software Co-design techniques, in order to balance performance and flexibility, particularly for resource-constrained systems. The methodology is applied to a gender recognition case study, using an ARM processor and FPGA fabric to create an embedded system that can process facial images in real-time.
机译:卷积神经网络(CNNS)具有广泛的应用,例如图像处理和自然语言处理。 灵感来自哺乳动物视觉皮质,已显示CNNS在许多计算机视觉挑战上实现令人印象深刻的结果,但通常具有大量的处理能力,并且没有时间限制。 本文介绍了使用硬件/软件共设计技术加速CNN的设计方法,以便平衡性能和灵活性,特别是对于资源受限系统。 该方法应用于性别识别案例研究,使用ARM处理器和FPGA面料创建一个嵌入式系统,可以实时处理面部图像。

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