Patent ID: 11893492
Assignee: REBELLIONS INC.
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 10:
11. A method for pruning of a neural processing device comprising at least one neural core of the neural processing device including processing circuitry for deep learning calculation tasks performing operations according to stored instructions, the method comprising:
receiving, by weight manipulator circuitry, a two-dimensional weight matrix as an input data signal;
checking, by the weight manipulator circuitry, a width of an SIMD input of a processing element;
initializing, by the weight manipulator circuitry, the two-dimensional weight matrix;
partitioning, by the weight manipulator circuitry, the two-dimensional weight matrix into at least one partitioned matrix based on the width of the SIMD input, wherein the at least one partitioned matrix contains at least one group;
counting a number of zero groups based on an initial threshold value to generate counting information;
selecting a partitioned matrix of the at least one partitioned matrix having a largest number of zero groups in the counting information as a reference partitioned matrix;
adjusting at least one threshold value of at least one remaining partitioned matrix of the at least one partitioned matrix other than the reference partitioned matrix to generate an updated threshold value;
pruning the at least one partitioned matrix based on the updated threshold value to generate a pruning matrix, wherein the pruning comprises generating a representative value of a first group and changing the first group to a zero group if the representative value is less than or equal to the updated threshold value;
transmitting, by the weight manipulator circuitry, the pruning matrix as an output data signal to the processing circuitry that includes a weight register temporarily storing the pruning matrix and performs an operation using the pruning matrix; and wherein the two-dimensional weight matrix contains at least one weight, wherein the at least one weight and the updated threshold value are trained via an artificial neural network, and wherein the updated threshold value is trained in a direction in which a number of zero groups is uniformly distributed for each partitioned matrix of the at least one partitioned matrix.