Huang-Chih Kuo

Title: Innovations by Huang-Chih Kuo

Introduction

Huang-Chih Kuo is a notable inventor based in Hsinchu, Taiwan. He has made significant contributions to the field of computing systems and neural networks. With a total of five patents to his name, Kuo's work focuses on enhancing the efficiency and performance of computational methods.

Latest Patents

One of Kuo's latest patents is a computing system and compressing method for neural network parameters. This innovation provides a method for obtaining multiple neural network parameters used in a neural network algorithm. The method groups at least two neural network parameters into an encoding combination, ensuring that the number of parameters in each combination is consistent. Each encoding combination is compressed independently, which helps save storage space and reduces excessive power consumption when accessing the parameters.

Another significant patent is the look-up table (LUT) compression method and LUT reading method for computation equipment and its host and device. In this patent, the host retrieves original data from an original LUT using an original table address. The original data is checked according to a reconstruction condition to obtain a check result, which is then used to convert the original data into reconstructed data. This data is written to a compressed LUT, and a mapping table is created to store the relationship among the original table address, the compressed table address, and the check result.

Career Highlights

Huang-Chih Kuo is currently employed at Neuchips Corporation, where he continues to innovate in the field of computing technology. His work has been instrumental in developing methods that enhance the efficiency of neural networks and computational systems.

Collaborations

Kuo collaborates with talented individuals such as Youn-Long Lin and Chao-Yang Kao, contributing to advancements in their respective fields.

Conclusion

Huang-Chih Kuo's innovative patents and contributions to computing systems demonstrate his commitment to advancing technology. His work not only enhances the efficiency of neural networks but also paves the way for future innovations in the field.