Ajou University Industry-Academic Cooperation Foundation

Tae Sun Kim

Myung Hoon Sunwoo

A convolution method for high speed deep learning according to the present invention includes (a) a step in which a feature map receiving unit of the convolution system receives a feature map configured by N channels; (b) a step in which a main controller of the convolution system selects a '0'-th channel from the feature map configured by N channels; (c) a step in which the main controller confirms a coordinate in which x, y coordinate is “0”, from the feature map of the “0”-th channel; (d) a coarse step in which a convolution calculating unit of the convolution system performs a convolution operation and a rectified linear unit (ReLU) operation while shifting by 2 in a horizontal direction and a vertical direction in the feature map; (e) a step in which the channel switching unit of the convolution system switches the channel to a subsequent channel when the coarse step is completed for the feature map of the “0”-th channel; (g) a step in which the main controller determines whether the switched channel is greater or less than N; and (g) a step in which if the channel switched in step (0 is greater than N, the main controller determines that the convolution operation for all channels has been completed and outputs the feature map by means of a feature map output unit. By doing this, the convolution operation which occupies most of the convolution neural network is reduced to increase inference speed in the deep learning.

Convolution method for high speed deep learning

Disclosed is a high efficiency video coding (HEVC) encoding device including a candidate group updater configured to select a plurality of representative modes as a candidate group from among intra-prediction modes and update the candidate group using a plurality of minimum modes selected from the candidate group, the plurality of representative modes each representing a range where there is an optimal mode, and an optimal mode selector configured to select any one mode as an optimal mode from among a plurality of minimum modes selected from the updated candidate group.

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Anseong-si, South Korea

Tae Sun Kim