Near memory sparse matrix computation in deep neural network

A method for controlling sparse matrix computation in deep learning, the method comprising:

• accessing a weight matrix from a random-access memory, the weight matrix comprising a plurality of rows;
• identifying a row of the weight matrix, the row comprising a plurality of weights;
• determining whether a weight in the plurality of weights has a non-zero value;
• in response to determine that the weight has the non-zero value, determining that a row value for the row is one;
• and
• sending the row value to a buffer configured to store the row value, the row value used to allow read of the plurality of weights from the random-access memory.

One or more non-transitory computer-readable media storing instructions executable to perform operations for accelerating a deep neural network (DNN) layer, the operations comprising:

• accessing a weight matrix from a random-access memory, the weight matrix comprising a plurality of rows;
• identifying a row of the weight matrix, the row comprising a plurality of weights;
• determining whether a weight in the plurality of weights has a non-zero value;
• in response to determine that the weight has the non-zero value, determining that a row value for the row is one;
• and
• sending the row value to a buffer configured to store the row value, the row value used to allow read of the plurality of weights from the random-access memory.