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DEEP LEARNING IMAGE PROCESSING SYSTEMS USING MODULARLY CONNECTED CNN BASED INTEGRATED CIRCUITS

机译:基于模块化连接的CNN集成电路的深度学习图像处理系统

摘要

A deep learning image processing system contains at least first and second groups of cellular neural networks (CNN) based integrated circuits (ICs). The first group and the second group are operatively connected in parallel via a network bus. CNN based ICs within each of the first and second groups are operatively connected in series via the network bus. The first group is configured for performing convolutional operations in respective portions of a deep learning model for extracting features out of a first subsection of input data. The second group is configured for performing convolutional operations in respective portions of the deep learning model for extracting features out of a second subsection of the input data. The deep learning model is divided into a plurality of consecutive portions being handled by the respective CNN based ICs. The input data is partitioned into at least first and second subsections.
机译:深度学习图像处理系统至少包含第一组和第二组基于细胞神经网络(CNN)的集成电路(IC)。第一组和第二组经由网络总线可操作地并联连接。第一和第二组中的每个中基于CNN的IC通过网络总线可操作地串联连接。第一组被配置为在深度学习模型的各个部分中执行卷积运算,以从输入数据的第一子部分中提取特征。第二组被配置为在深度学习模型的各个部分中执行卷积运算,以从输入数据的第二子部分中提取特征。深度学习模型分为多个连续部分,分别由基于CNN的IC处理。输入数据至少分为第一和第二子部分。

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