Patent ID: 11948090
Assignee: TENCENT AMERICA LLC
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 8:
9. An apparatus, comprising:
processing circuitry configured to:
partition a first super-block of a plurality of super-blocks into blocks, each of the blocks having a respective sub compression rate loss, a respective sub unification distortion loss, and a respective sub computation speed loss;
obtain a compression rate loss of the first super-block, the compression rate loss of the first super-block being a sum of the sub compression rate losses of the blocks, the sub compression rate loss of each of the blocks being based on a standard deviation of absolute values of features in the respective block:
obtain a unification distortion loss of the first super-block, the unification distortion loss of the first super-block being a sum of the sub unification distortion losses of the blocks, the sub unification distortion loss of each of the blocks being based on a reciprocal of a multiplication value of a height, a width, and a depth for the respective block;
obtain a computation speed loss of the first super-block, the computation speed loss of the first super-block being a sum of the sub computation speed losses of the blocks, the sub computation speed loss of each of the blocks being based on a number of multiplication operations in a computation using a unifying method for the respective block;
determine a respective optimal index order and a respective optimal unifying method for each of super-blocks partitioned from a feature map based on feature unification losses, the feature map being generated by passing a first input through a deep neural network (DNN), the feature unification losses including a first feature unification loss that is equal to a sum of the compression rate loss, the unification distortion loss, and the computation speed loss of the first super-block;
determine a selective structured unification (SSU) layer based on the respective optimal index order and the respective optimal unifying method for each of the super-blocks, the SSU layer being added after an extraction layer of the DNN that generates the feature map to form an updated DNN, the SSU layer being configured to perform unification operations on the feature map; and
determine a first estimated output that is generated by passing the first input through the updated DNN.