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METHOD AND APPARATUS FOR PERFORMING OPERATION OF CONVOLUTIONAL LAYERS IN CONVOLUTIONAL NEURAL NETWORK

机译:用于在卷积神经网络中执行卷积层操作的方法和装置

摘要

Disclosed are a method and an apparatus for performing an operation of a convolutional layer in a convolutional neural network. The method comprises: reading unfolded feature data provided to the convolution layer and an original convolution kernel of the convolutional layer from a dynamic random access memory (DRAM); folding the unfolded feature data in at least one dimension of width and height to generate folded feature data; pre-processing the folded feature data and the original convolution kernel; storing the pre-processed folded feature data into a static random-access memory (SRAM); folding the pre-processed original convolution kernel in the at least one dimension to generate one or more folded convolution kernels corresponding to the original convolution kernel; storing the one or more folded convolution kernels in the SRAM; and reading the pre-processed folded feature data and the one or more folded convolution kernels from the SRAM into a calculation unit for convolving the pre-processed folded feature data with the one or more folded convolution kernels. By means of the method and/or apparatus in accordance with embodiments of the present disclosure, channel utilization may be improved, cache occupancy may be reduced, and operation efficiency may be improved.
机译:公开了一种用于在卷积神经网络中执行卷积层的操作的方法和装置。该方法包括:从动态随机存取存储器(DRAM)读取提供给卷积层的展开特征数据和卷积层的原始卷积核;将展开的特征数据折叠在宽度和高度的至少一个尺寸中以产生折叠的特征数据;预处理折叠的特征数据和原始卷积内核;将预处理的折叠功能数据存储到静态随机存取存储器(SRAM)中;将预处理的原始卷积内核折叠在至少一个维度中以生成与原始卷积内核对应的一个或多个折叠的卷积内核;在SRAM中存储一个或多个折叠卷积内核;从SRAM读取预处理的折叠特征数据和一个或多个折叠卷积内核,进入计算单元,用于将预处理的折叠特征数据与一个或多个折叠的卷积内核卷积。借助于根据本公开的实施例的方法和/或装置,可以提高信道利用,可以减少高速缓存占用,并且可以提高操作效率。

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