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POWER-EFFICIENT DEEP NEURAL NETWORK MODULE CONFIGURED FOR EXECUTING A LAYER DESCRIPTOR LIST

机译:高效的深层神经网络模块,用于执行层描述符列表

摘要

The performance of a neural network (NN) and/or deep neural network (DNN) can limited by the number of operations being performed as well as management of data among the various memory components of the NN/DNN. Using a directed line buffer that operatively inserts one or more shifting bits in data blocks to be processed, data read/writes to the line buffer can be optimized for processing by the NN/DNN thereby enhancing the overall performance of a NN/DNN. Operatively, an operations controller and/or iterator can generate one or more instructions having a calculated shifting bit(s) for communication to the line buffer. Illustratively, the shifting bit(s) can be calculated using various characteristics of the input data as well as the NN/DNN inclusive of the data dimensions. The line buffer can read data for processing, insert the shifting bits and write the data in the line buffer for subsequent processing by cooperating processing unit(s).
机译:神经网络(NN)和/或深度神经网络(DNN)的性能可能会受到正在执行的操作数量以及NN / DNN的各种内存组件之间的数据管理的限制。使用可操作地在要处理的数据块中插入一个或多个移位位的有向线缓冲区,可以优化对线缓冲区的数据读/写操作,以供NN / DNN处理,从而增强NN / DNN的整体性能。在操作上,操作控制器和/或迭代器可以生成一个或多个指令,该指令具有计算出的移位位以与行缓冲器通信。说明性地,可以使用输入数据的各种特性以及包括数据维度的NN / DNN来计算移位位。行缓冲器可以读取数据进行处理,插入移位位,并将数据写入行缓冲器中以通过协作处理单元进行后续处理。

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