Method and apparatus with neural network processing

1. A neural network device comprising:

a shift register circuit comprising registers configured to, in each cycle of plural cycles, transfer stored data to a next register and store new data received from a previous register;

a control circuit configured to sequentially input data of input activations included in an input feature map into the shift register circuit in a preset order; and

a processing circuit, comprising crossbar array groups that receive the input activations from at least one of the registers and perform a multiply-accumulate (MAC) operation with respect to the received input activation and weights, configured to select at least some of operation results output from the crossbar array groups at a preset number of cycles to be converted and accumulate and add the at least some operation results using a result of the converted to obtain an output activation in an output feature map.

2. The neural network device of claim 1 , wherein the control circuit is further configured to receive a 1-bit zero mark on each of the plural cycles, and in response to a value of the zero mark being 1 , to control the crossbar array groups to omit a MAC operation with respect to input activations corresponding to the zero mark.

3. The neural network device of claim 1 , wherein crossbar arrays included in one crossbar array group of the crossbar array groups share a same input activation.

4. The neural network device of claim 3 , wherein each of the crossbar arrays comprises:

a plurality of row lines;

a plurality of column lines intersecting the plurality of row lines; and

memory cells respectively disposed at the intersections of the plurality of row lines and the plurality of column lines, and configured to store the weights included in a weight kernel.

5. The neural network device of claim 3 , wherein the processing circuit is further configured to obtain a first output activation using an operation result output from one of the crossbar arrays, and obtain a second output activation using an operation result output from another of the crossbar arrays.

6. The neural network device of claim 3 , wherein a number of the crossbar arrays included in the one crossbar array group corresponds to a width of a weight kernel.

7. The neural network device of claim 1 , wherein a number of registers that transfer input activation to the crossbar array groups from the registers corresponds to a height of a weight kernel.

8. The neural network device of claim 1 , wherein the processing circuit is further configured to convert the selected operation results into a 2′s complement format, and accumulate and add the converted operation results to obtain the output activation.

9. The neural network device of claim 1 , wherein

the processing circuit comprises an output line through which the output activation is output, and

the output line corresponds to an output of one of a plurality of layers constituting a neural network, and is directly connected to an input line of a next layer.

10. The neural network device of claim 9 , wherein the next layer comprises either one or both of a convolution layer and a pooling layer.

11. A method of a neural network device, the method comprising:

sequentially inputting input activations included in an input feature map into a shift register circuit in a preset order;

receiving an input activation of the input activations from at least one of a plurality of registers, of the shift register circuit, corresponding to a corresponding crossbar array group of crossbar array groups and performing a multiply-accumulate (MAC) operation on the received input activation and weights; and

obtaining an output activation included in an output feature map by selecting at least some of operation results output from the crossbar array groups at a preset number of cycles to be converted and accumulating and adding the at least some of operation results based on a result of the converted.

12. The method of claim 11 , further comprising:

receiving a 1-bit zero mark on each cycle of the sequentially inputting of the input activations; and

in response to the a value of the zero mark being 1 , controlling the crossbar array groups to omit the MAC operation with respect to input activations corresponding to the zero mark.

13. The method of claim 11 , wherein crossbar arrays included in one crossbar array group of the crossbar array groups share a same input activation.

14. The method of claim 13 , wherein each of the crossbar arrays comprises:

a plurality of row lines;

a plurality of column lines intersecting the plurality of row lines; and

memory cells respectively disposed at the intersections of the plurality of row lines and the plurality of column lines, and configured to store the weights of a weight kernel.

15. The method of claim 13 , further comprising:

obtaining a first output activation using an operation result output from one of the crossbar arrays; and

obtaining a second output activation using an operation result output from another crossbar array of the crossbar arrays.

16. The method of claim 13 , wherein a number of the crossbar arrays included in the one crossbar array group corresponds to a width of a weight kernel.

17. The method of claim 11 , wherein a number of registers that transfer input activation to the crossbar array groups from the plurality of registers corresponds to a height of a weight kernel.

18. The method of claim 11 , wherein the obtaining the output activation comprises:

converting the selected operation results into a 2′s complement format; and

accumulating and adding the converted operation results.

19. The method of claim 11 , further comprising outputting the output activation via an output line, wherein the output line corresponds to an output of one of a plurality of layers constituting a neural network, and is directly connected to an input line of a next layer.

20. The method of claim 19 , wherein the next layer comprises either one or both of a convolutional layer and a pooling layer.