Steve Haihang Dai

Title: Innovations by Steve Haihang Dai

Introduction

Steve Haihang Dai is an accomplished inventor based in Union City, CA, with a focus on advancements in neural network technology. He holds three patents that contribute significantly to the field of artificial intelligence and machine learning. His work aims to enhance the efficiency and accuracy of neural networks, which are increasingly vital in various applications.

Latest Patents

One of his latest patents is titled "Fine-grained per-vector scaling for neural network quantization." This innovation addresses the energy consumption of neural networks used in autonomous vehicles, speech recognition, real-time language translation, and online search optimizations. By quantizing parameters, the memory required for storage is reduced, and power consumption during operation is minimized. The method involves quantizing smaller sets of parameters using a shared scale factor, which improves accuracy compared to quantizing larger sets.

Another significant patent is "Optimally clipped tensors and vectors." This invention focuses on reducing power consumption and accelerating processing within neural networks through quantization. It emphasizes performing quantization-aware training (QAT) by dynamically quantizing tensors using optimal clipping scalars. This approach minimizes the mean squared error (MSE) of the quantized operation, ensuring that accuracy is maintained while reducing the number of bits used to represent values.

Career Highlights

Steve is currently employed at Nvidia Corporation, a leading company in graphics processing and AI technology. His contributions to the field have positioned him as a key player in the development of efficient neural network operations.

Collaborations

Steve collaborates with notable colleagues, including Brucek Kurdo Khailany and Rangharajan Venkatesan, who share his commitment to advancing technology in the realm of artificial intelligence.

Conclusion

Steve Haihang Dai's innovative patents and contributions to neural network technology reflect his dedication to improving efficiency and accuracy in artificial intelligence applications. His work continues to influence the future of technology in significant ways.