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A Novel Convolution Computing Paradigm Based on NOR Flash Array With High Computing Speed and Energy Efficiency

机译:基于NOR闪存阵列的新型卷积计算速度,具有高计算速度和能量效率

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Convolution is one of the key operations in signal processing and machine learning applications. In this paper, we propose a novel convolution computing paradigm based on the NOR Flash array (NFA) that is capable of executing the 2-D convolution computing in one clock cycle. In order to demonstrate the feasibility and efficiency of the proposed convolution computing paradigm, the feature extraction task on an image with the size of 20 x 20 is executed using the NFA structure. We also prove the NOR Flash-driven convolution computing is capable of processing the image with a larger size. This paper presents a new approach to realize convolution computing with high speed and energy efficiency for the signal processing and convolution neural network.
机译:卷积是信号处理和机器学习应用中的关键操作之一。在本文中,我们提出了一种基于NOR闪存阵列(NFA)的新型卷积计算范式,其能够在一个时钟周期中执行二维卷积计算。为了证明所提出的卷积计算范例的可行性和效率,使用NFA结构执行具有大小为20×20的图像上的特征提取任务。我们还证明了NOR闪存驱动的卷积计算能够以更大的尺寸处理图像。本文介绍了一种新的方法,以实现高速和能源效率的卷积计算和卷积神经网络。

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