Xiaotian Gao

Title: Innovations of Xiaotian Gao in Reinforcement Learning

Introduction

Xiaotian Gao is a prominent inventor based in Beijing, China. He has made significant contributions to the field of network function virtualization (NFV) through his innovative work in reinforcement learning algorithms. His expertise and dedication to advancing technology have led to the development of a unique patent that addresses power management in NFV servers.

Latest Patents

Xiaotian Gao holds a patent titled "Hierarchical reinforcement learning algorithm for NFV server power management." This patent describes methods and apparatus for a hierarchical reinforcement learning (RL) algorithm designed to optimize power management in NFV servers. The invention involves training a first RL model at a first layer by adjusting the frequency of the core of a processor while performing a workload. This process results in a first trained RL model that operates in inference mode. Simultaneously, a second RL model is trained at a second level in the RL hierarchy, which adjusts both the core frequency and the frequency of processor circuitry external to the core. This innovative approach allows for efficient power management during online operations, particularly for NFV workloads.

Career Highlights

Xiaotian Gao is currently employed at Intel Corporation, where he continues to push the boundaries of technology. His work focuses on enhancing the efficiency of NFV servers through advanced algorithms. His contributions have been recognized within the industry, showcasing his commitment to innovation and excellence.

Collaborations

Xiaotian has collaborated with notable colleagues, including Zhu Zhou and Chris MacNamara. These partnerships have fostered a collaborative environment that encourages the exchange of ideas and the development of cutting-edge technologies.

Conclusion

Xiaotian Gao's innovative work in reinforcement learning algorithms has made a significant impact on the field of network function virtualization. His patent for hierarchical reinforcement learning demonstrates his commitment to advancing technology and improving power management in NFV servers. His contributions, along with his collaborations, highlight the importance of teamwork in driving innovation forward.