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專利授權區
專利名稱(中)	使用環張量之深度神經網路加速方法及其系統
專利名稱(英)	DEEP NEURAL NETWORK ACCELERATING METHOD USING RING TENSORS AND SYSTEM THEREOF
專利家族	中華民國：I775296 大陸：CN113780538A(公開號) 美國：2021-0383198(公開號)
專利權人	國立清華大學 100%
發明人	黃朝宗
技術領域	資訊工程,電子電機

專利摘要(英)
A deep neural network accelerating method using a plurality of ring tensors including a ring tensor setting step, a ring tensor convolution calculating step and a non-linear activation function calculating step. The ring tensor setting step is performed to set an input feature tensor and a weight tensor of a convolutional network. The input feature tensor includes a plurality of input feature ring elements, and the weight tensor includes a plurality of weight ring elements. The ring tensor convolution calculating step is performed to calculate the input feature ring elements of the input feature tensor and the weight ring elements of the weight tensor according to a ring multiplication calculating step and a ring addition calculating step to generate a plurality of convolution feature ring elements of a convolution feature tensor. The non-linear activation function calculating step is performed to execute a directional rectified linear unit (ReLU) on the convolution feature ring elements of the convolution feature tensor to generate output feature ring elements. The ring multiplication calculating step includes performing a ring multiplication on one of the input feature ring elements and one of the weight ring elements to generate a multiplication output ring element. A multiplication output component of the multiplication output ring element is obtained by performing a component-wise product on an input feature component of the one of the input feature ring elements and a weight component of the one of the weight ring elements. The ring addition calculating step includes performing a ring addition on a plurality of the multiplication output ring elements to generate one of the convolution feature ring elements. A convolution feature component of each of the convolution feature ring elements is obtained by performing a component-wise addition on a plurality of the multiplication output components of the multiplication output ring elements. Therefore, the present disclosure provides a novel ring algebra which adopts the component-wise product for the ring multiplication to minimize complexity and directional ReLU for ring non-linearity to achieve great quality.

專利摘要(英)

A deep neural network accelerating method using a plurality of ring tensors including a ring tensor setting step, a ring tensor convolution calculating step and a non-linear activation function calculating step. The ring tensor setting step is performed to set an input feature tensor and a weight tensor of a convolutional network. The input feature tensor includes a plurality of input feature ring elements, and the weight tensor includes a plurality of weight ring elements. The ring tensor convolution calculating step is performed to calculate the input feature ring elements of the input feature tensor and the weight ring elements of the weight tensor according to a ring multiplication calculating step and a ring addition calculating step to generate a plurality of convolution feature ring elements of a convolution feature tensor. The non-linear activation function calculating step is performed to execute a directional rectified linear unit (ReLU) on the convolution feature ring elements of the convolution feature tensor to generate output feature ring elements. The ring multiplication calculating step includes performing a ring multiplication on one of the input feature ring elements and one of the weight ring elements to generate a multiplication output ring element. A multiplication output component of the multiplication output ring element is obtained by performing a component-wise product on an input feature component of the one of the input feature ring elements and a weight component of the one of the weight ring elements. The ring addition calculating step includes performing a ring addition on a plurality of the multiplication output ring elements to generate one of the convolution feature ring elements. A convolution feature component of each of the convolution feature ring elements is obtained by performing a component-wise addition on a plurality of the multiplication output components of the multiplication output ring elements. Therefore, the present disclosure provides a novel ring algebra which adopts the component-wise product for the ring multiplication to minimize complexity and directional ReLU for ring non-linearity to achieve great quality.

聯絡資訊
承辦人姓名	李曉琪
承辦人電話	03-5715131 #31061
承辦人Email	hsiaochi@mx.nthu.edu.tw