CAPACITIVE ARTIFICIAL NEURAL NETWORKS

­APPLICATIONS

Applications include pattern recognition, image processing, natural language understanding, robotics, and other fields where machine learning and artificial intelligence techniques are applied.

APPLICATIONSTECHNOLOGY DESCRIPTION

The invention introduces a technology that utilizes capacitative components to implement artificial neural networks, enabling advanced computing systems with improved efficiency and performance.

It addresses the need for more efficient and scalable artificial neural network architectures. It aims to provide a solution that allows for faster and more energy-efficient information processing, enabling the development of advanced machine learning and artificial intelligence systems.

The patent describes a novel approach that incorporates capacitative components into the design of artificial neural networks. Capacitors are utilized as key elements in the network's structure, taking advantage of their ability to store and release electrical charge.

The capacitative artificial neural networks (CANNs) outlined in the patent leverage the inherent properties of capacitors to facilitate fast and parallel processing of information. The capacitors store electrical charges that represent synaptic weights, enabling the network to perform computations efficiently.

The invention further describes the integration of CANNs with other components such as transistors, amplifiers, and interconnects to create complete neural network systems. These systems demonstrate improved performance in terms of speed, energy efficiency, and scalability compared to traditional artificial neural network architectures.

ABOUT THE INVENTOR

 Dr. Qiangfei Xia is a professor of Electrical & Computer Engineering at UMass Amherst and head of the Nanodevices and Integrated Systems Laboratory. He received his Ph.D. in Electrical Engineering in 2007 from Princeton University 

  

AVAILABILITY:

Available for Licensing and/or Sponsored Research

  

DOCKET:

UMA 18-003

  

PATENT STATUS:

US Patent Issued 10,740,672

  

NON-CONFIDENTIAL INVENTION DISCLOSURE

 

LEAD INVENTOR:

Dr. Qiangfei Xia

 

CONTACT:

 

                                            

 

 

Patent Information: