Company Filing History:
Years Active: 2023
Title: Innovations of Sergey Voronov in Neural Networks
Introduction
Sergey Voronov is a notable inventor based in Chapel Hill, NC (US). He has made significant contributions to the field of artificial intelligence, particularly in the area of neural networks. His innovative work focuses on enhancing the efficiency and effectiveness of artificial neural networks through adaptive quantization techniques.
Latest Patents
Voronov holds a patent for "Adaptive quantization for neural networks." This patent describes methods, devices, systems, and instructions for adaptive quantization in an artificial neural network (ANN). The process involves calculating a distribution of ANN information, selecting a quantization function from a set of quantization functions based on this distribution, and applying the quantization function to generate quantized ANN information. The quantized information is then loaded into the ANN to generate an output. Notably, if the output does not sufficiently correlate with a known correct output, the system can recalculate the distribution of ANN information and reselect the quantization function based on the resampled distribution. This innovation is crucial for improving the performance of neural networks.
Career Highlights
Sergey Voronov is currently employed at Advanced Micro Devices Corporation, where he continues to work on cutting-edge technologies in the field of artificial intelligence. His expertise in neural networks and adaptive quantization has positioned him as a valuable asset in the tech industry.
Collaborations
Voronov collaborates with talented individuals such as Daniel I Lowell and Mayank Daga. Their combined efforts contribute to advancing the research and development of innovative solutions in artificial intelligence.
Conclusion
Sergey Voronov's contributions to the field of neural networks through his patent on adaptive quantization demonstrate his commitment to innovation and excellence. His work continues to influence the development of more efficient artificial intelligence systems.