The present disclosure relates to a communication method and system for converging a 5th-Generation (5G) communication system for supporting higher data rates beyond a 4th-Generation (4G) system with a technology for Internet of Things (IoT). The present disclosure may be applied to intelligent services based on the 5G communication technology and the IoT-related technology, such as smart home, smart building, smart city, smart car, connected car, health care, digital education, smart retail, security and safety services. A Processing Element (PE) implemented in an accelerator in a convolutional neural network, which includes a first buffer configured to transfer input data to one other PE, and a second buffer configured to transmit to an outside output data that is processed on the basis of the input data; and an operation unit configured to generate output data.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A Processing Element (PE) implemented in an accelerator in a convolutional neural network, comprising: a first buffer configured to transfer input data to a second PE; a second buffer configured to transmit output data, that is based on processing the input data, to an outside; and an operation unit configured to generate intermediate data by performing a first operation with respect to the input data, to store the intermediate data in the second buffer, to generate the output data by performing a second operation with respect to the intermediate data that is fed back from the second buffer, and to store the output data in the second buffer. 2. The PE of claim 1, wherein each of the first operation and the second operation is related to at least one of a convolution layer, a non-linear layer, and a normalization layer. 3. An accelerator including a plurality of Processing Elements (PEs) in a convolutional neural network, wherein each of the plurality of PEs comprises: a first buffer configured to transfer input data to one other PE of the plurality of PEs; a second buffer configured to transmit output data, that is based on processing the input data, to an outside; and an operation unit configured to generate intermediate data by performing a first operation with respect to the input data, to store the intermediate data in the second buffer, to generate the output data by performing a second operation with respect to the intermediate data that is fed back from the second buffer, and to store the output data in the second buffer. 4. The accelerator of claim 3, wherein each of the first operation and the second operation is related to at least one of a convolution layer, a non-linear layer, and a normalization layer. 5. The accelerator of claim 3, further comprising: a third buffer, and a pooling unit configured to receive plural pieces of output data that are transmitted from the plurality of PEs, to perform a pooling operation with respect to the plural pieces of output data, and to transmit the output data to the third buffer. 6. The accelerator of claim 5, further comprising a buffer controller configured to control the third buffer, and to transmit final data that is accumulated in the third buffer to an external memory if the final data is equal to or larger than a predetermined size. 7. The accelerator of claim 3, wherein the plurality of PEs are grouped into one or more matrixes of a predetermined matrix form. 8. The accelerator of claim 7, further comprising a pooling unit shared by each of the one or more matrixes. 9. The accelerator of claim 3, wherein at least one of the plurality of PEs further comprises a pooling unit configured to receive adjacent output data from at least one adjacent PEs, and to perform a pooling operation with respect to the adjacent output data and the output data stored in the second buffer. 10. The accelerator of claim 3, wherein the accelerator is controlled in accordance with a command from an external processor. 11. The accelerator of claim 3, wherein the output data is transmitted to the outside, wherein the outside is any one of the plurality of PEs, a pooling unit, or an external memory. 12. A method for operating an accelerator that includes a plurality of Processing Elements (PEs) in a convolutional neural network, comprising: storing an input image in a first buffer, as input data, and transferring the input data to one other PE of the plurality of PEs; generating intermediate data by performing a first operation with respect to the input data, and storing the intermediate data in a second buffer; generating output data by performing a second operation with respect to the intermediate data that is fed back from the second buffer, and storing the output data in the second buffer; and outputting the output data from the second buffer to an outside. 13. The method of claim 12, wherein each of the first operation and the second operation is related to at least one of a convolution layer, a non-linear layer, and a normalization layer. 14. The method of claim 12, further comprising receiving plural pieces of output data that are transmitted from the plurality of PEs, performing a pooling operation with respect to the plural pieces of output data, and transmitting the output data to a third buffer. 15. The method of claim 12, further comprising grouping the plurality of PEs into one or more matrices of a predetermined matrix form. 16. The method of claim 12, further comprising receiving adjacent output data from at least one adjacent PEs and performing a pooling operation with respect to the adjacent output data and the output data stored in the second buffer. 17. The method of claim 12, wherein the outside is any one of the plurality of PEs, a pooling unit, or an external memory. 18. The method of claim 14, further comprising transmitting final data that is accumulated in the third buffer to an external memory if the final data is equal to or larger than a predetermined size. 19. The PE of claim 1, further comprises a pooling unit configured to receive adjacent output data from at least one adjacent PE, to perform a pooling operation with respect to the adjacent output data and the output data stored in the second buffer. 20. The PE of claim 1, wherein the outside is at least one of a third PE, a pooling unit, or an external memory.