Patents by Inventor Sitong FENG

Sitong FENG has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).

  • Methods and apparatuses for high performance and accuracy fixed-point scale implementation
    Patent number: 12293275
    Abstract: A method to implement a fixed-point scale layer in a neural network for data processing is provided in the present disclosure. The method includes: receiving fixed-point input data over a channel of a standalone floating-point scale layer, and converting the floating-point input data into fixed-point input data of the standalone floating-point scale layer; obtaining fixed-point quantization parameters in each channel based on the input data and floating-point parameters ?i, ?i in each channel; converting the standalone floating-point scale layer based on the fixed-point quantization parameters into a fixed-point scale layer for processing the fixed-point input data to generate fixed-point output data; and mapping the fixed-point scale layer to a fixed-point convolution layer and the computation of convolution is done by matrix multiplication that can be executed on a GEMM engine.
    Type: Grant
    Filed: July 6, 2021
    Date of Patent: May 6, 2025
    Assignee: BEIJING DAJIA INTERNET INFORMATION TECHNOLOGY CO., LTD.
    Inventors: Ming Kai Hsu, Sitong Feng
  • METHODS AND DEVICES FOR NEURAL NETWORK QUANTIZATION USING TEMPORAL PROFILING
    Publication number: 20230084000
    Abstract: Methods and apparatuses are provided for temporal profiling for neural network quantization. The method includes: obtaining a neural network that comprises anode connected to different paths at different time periods; obtaining node outputs for the node at the different time periods; determining statistic properties of the node outputs at the different time periods; and determining activation ranges of the node outputs based on the statistic properties.
    Type: Application
    Filed: September 15, 2021
    Publication date: March 16, 2023
    Applicant: KWAI INC.
    Inventors: Ming Kai HSU, Chao YANG, Yue MA, Sikai WANG, Sitong FENG, Wenhui CAO, Danqing LI, Hui ZHONG, Lingzhi LIU
  • Methods and apparatuses for high performance and accuracy fixed-point scale implementation
    Publication number: 20230010981
    Abstract: A method to implement a fixed-point scale layer in a neural network for data processing is provided in the present disclosure. The method includes: receiving fixed-point input data over a channel of a standalone floating-point scale layer, and converting the floating-point input data into fixed-point input data of the standalone floating-point scale layer; obtaining fixed-point quantization parameters in each channel based on the input data and floating-point parameters ?i, ?i in each channel; converting the standalone floating-point scale layer based on the fixed-point quantization parameters into a fixed-point scale layer for processing the fixed-point input data to generate fixed-point output data; and mapping the fixed-point scale layer to a fixed-point convolution layer and the computation of convolution is done by matrix multiplication that can be executed on a GEMM engine.
    Type: Application
    Filed: July 6, 2021
    Publication date: January 12, 2023
    Applicant: KWAI INC.
    Inventors: Ming Kai HSU, Sitong FENG