Tainan, Taiwan

Chao-Hong Chen


Average Co-Inventor Count = 3.4

ph-index = 1

Forward Citations = 2(Granted Patents)


Company Filing History:


Years Active: 2019-2022

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2 patents (USPTO):Explore Patents

Title: Innovations of Chao-Hong Chen

Introduction

Chao-Hong Chen is a notable inventor based in Tainan, Taiwan. He has made significant contributions to the field of technology, particularly in the areas of deep neural networks and power estimation for FPGA-based systems. With a total of two patents to his name, Chen's work reflects a commitment to advancing technological capabilities.

Latest Patents

Chao-Hong Chen's latest patents include a method and electronic device for selecting deep neural network hyperparameters. This innovative method involves sampling a variety of hyperparameter configurations from multiple ranges. By training a target neural network model with these configurations, the method predicts final accuracies to recommend the best hyperparameter settings for further training. Another significant patent is the FPGA-based system power estimation apparatus and method. This invention provides a way to estimate the power consumption of a target intellectual property circuit using a power analysis circuit integrated within an FPGA. This approach allows for accurate power value determination based on the internal operation-state signals of the target circuit.

Career Highlights

Chao-Hong Chen is affiliated with the Industrial Technology Research Institute, where he applies his expertise in technology and innovation. His work has contributed to the development of advanced systems that enhance the efficiency and performance of electronic devices.

Collaborations

Some of Chao-Hong Chen's coworkers include Yung-Chieh Lin and Shih-Che Lin. Their collaborative efforts in research and development have furthered the impact of their innovations in the technology sector.

Conclusion

Chao-Hong Chen's contributions to technology through his patents demonstrate his innovative spirit and dedication to improving electronic systems. His work continues to influence advancements in deep learning and power estimation methodologies.

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